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      "raw_text": "Contact information Nguyén Dinh Thanh Email: dinhthanhhcmut.edu.vn 2"
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      "raw_text": "Data Delivery modes - Pull-only - Push-only - Hybrid Frequency - Periodic - Conditional - Ad-hoc or irregular Communication Methods - Unicast - One-to-many Note: not all combinations make sense Ch.1/11"
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      "raw_text": "Example EMP ASG ENO ENAME TITLE ENO PNO RESP DUR E1 J. Doe Elect. Eng E1 P1 Manager 12 E2 M. Smith Syst. Anal. E2 P1 Analyst 24 E3 A.Lee Mech. Eng E2 P2 Analyst 6 E4 J. Miller Programmer E3 P3 Consultant 10 E5 B. Casey Syst. Anal. E3 P4 Engineer 48 E6 L. Chu Elect. Eng. E4 P2 Programmer 18 E7 R. Davis Mech. Eng E5 P2 Manager 24 E8 J. Jones Syst. Anal. E6 P4 Manager 48 E7 P3 Engineer 36 E8 P3 Manager 40 PROJ PAY TITLE SAL PNO PNAME BUDGET P1 Instrumentation 150000 Elect. Eng 40000 P2 Database Develop 135000 Syst. Anal. 34000 P3 CAD/CAM 250000 Mech. Eng 27000 P4 Maintenance 310000 Programmer 24000 Ch.1/14"
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      "raw_text": "Reality Distributed DBMS User Query User DBMS Application Software DBMS software Communication DBMS Software Subsystem User DBMS User Application Software Query DBMS Software User Query Ch.1/17"
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      "raw_text": "Types of Transparency 1 Data independence Network transparency (or distribution transparency) - Location transparency - Naming transparency Replication transparency Ch.1/18"
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      "raw_text": "Issue is database scaling Emergence and technologies workstation l 1 - Demise of Grosh's Iaw - Client-server model of computing Data communication l cost vs telecommunication cost Ch.1/22"
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      "raw_text": "Relationship Between Issues Directory Management 4 Query Distribution Reliability Processing Design A Concurrency Control Deadlock Management Ch.1/25"
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      "raw_text": "Related Issues Operating System Support - Operating system with p proper support for database operations - Dichotomy between general purpose processing requirements  and database processing requirements Open Systems and - Distributed Multidatabase Systems - More probable scenario Ch.1/26"
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      "raw_text": "Correctness of Fragmentation Completeness Decomposition of relation R into fragments R, R,, ..., R, is complete if and only if each data item in R can also be found in some Rj Reconstruction If relation R is decomposed into fragments R, R,, ..., R.. then there should exist some relational operator v such that R Disjointness  If relation R is decomposed into fragments R, R,, ..., R., and data item d, is in R, then d; should not be in any other fragment Rk(kj) 8"
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      "raw_text": "Comparison of Replication Alternatives Full replication Partial replication Partitioning QUERY Easy Same difficulty PROCESSING DIRECTORY Easy or Same difficulty MANAGEMENT nonexistent CONCURRENCY Moderate Difficult Easy CONTROL RELIABILITY Very high High Low Possible Possible REALITY Realistic application application 10"
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      "raw_text": "Fragmentation Horizontal Fragmentation (HF) Primary Horizontal Fragmentation (PHF) Derived Horizontal Fragmentation (DHF Vertical Fragmentation (VF) Hybrid Fragmentation (HF) 11"
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      "raw_text": "PHF - Information Requirements Database Information relationship PAY TITLE, SAL L1 EMP PROJ ENO, ENAME, TITLE PNO, PNAME, BUDGET, LOC L2 ASG ENO, PNO, RESP, DUR cardinality of each relation: card(R) 12"
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      "raw_text": "PHF - Information Requirements Application Information simple predicates : Given R[A., A2, ..., A,], a simple predicate p is Pj : Aj 6Value where e {=,<,,>,=,}, Value Di and D, is the domain of A.. For relation R we define Pr ={P, P, ...,Pm} Example : PNAME = \"Maintenance BUDGET < 200000 minterm predicates : Given R and Pr = {P1 P, ...,Pm} define M ={m,m,...,m,} as M={ m;1m;= where p* = p or p* = -(p)) 13"
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      "raw_text": "Completeness of Simple Predicates According to (1) Pr={LOC=\"Montreal\",LOC=\"New York\",LOC=\"Paris\"} which is not complete with respect to (2) Modify Pr ={LOC=\"Montreal\",LOC=\"New York\",LOC=\"Paris\" BUDGET<200000,BUDGET>200000} which is complete 20"
    },
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        "timestamp": "2025-10-31T13:42:21+07:00"
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      "raw_text": "Minimality of Simple Predicates If a predicate influences how fragmentation is performed (i.e., causes a fragment f to be further fragmented into, say, f and f) then there should be at least one application that accesses f, and f: differently. In other words, the simple predicate should be relevant in determining a fragmentation. If all the predicates of a set Pr are relevant, then Pr is minimal acc(m) acc(m) not card( fi) card(fj) 21"
    },
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        "timestamp": "2025-10-31T13:42:23+07:00"
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      "raw_text": "Minimality of Simple Predicates Example : Pr ={LOC=\"Montreal\",LOC=\"New York\" LOC=\"Paris\" BUDGET<200000,BUDGET>200000} is minimal (in addition to being complete). However, if we add PNAME = \"Instrumentation' then Pr is not minimal 22"
    },
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      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_023.png",
      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_023.png",
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        "timestamp": "2025-10-31T13:42:26+07:00"
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      "raw_text": "COM MIN Algorithm Given: a relation R and a set of simple predicates Pr Output: a complete and minimal set of simple predicates Pr' for Pr Rule 1: a relation or fragment is partitioned into at least two parts which are accessed differently by at least one application. 23"
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      "chapter_num": 2,
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      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_024.png",
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      "raw_text": "COM MiN Algorithm 1  Initialization : find a p; in Pr such that p; partitions R according to Rule 1 set Pr'=pj ;Pr Pr-{p};F+{f} 2 Iteratively add predicates to Pr' until it is complete find a p: in Pr such that p; partitions some fk defined according to minterm predicate over Pr' according to Rule 1 set Pr'=Pr'+{p};Pr=Pr-{p};F=F+{f}  If exist p, in Pr' which is nonrelevant then Pr'= Pr -{p} F = F-{f} 24"
    },
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      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_025.png",
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_025.png",
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        "timestamp": "2025-10-31T13:42:33+07:00"
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      "raw_text": "PHORlZONTAL Agorithm Makes use of COM MIN to perform fragmentation. Input: a relation R ? and a set of simple predicates Pr Output: a set of minterm predicates M according to which relation R is to be fragmented 0 Pr' = COM MIN (R,Pr) @ determine the set M of minterm predicates @ determine the set / of implications among P; Pr 4 eliminate the contradictory minterms from M 25"
    },
    {
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      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_026.png",
      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_026.png",
        "page_index": 71,
        "language": "en",
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        "timestamp": "2025-10-31T13:42:36+07:00"
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      "raw_text": "PHF - Example Two candidate relations : PAY and PROJ Fragmentation of relation PAY Application: Check the salary info and determine raise. Employee records kept at two sites application run at two sites Simple predicates P1 : SAL  30000 P,: SAL > 30000 Pr = {p,} which is complete and minimal Pr'=Pr Minterm predicates m1 : (SAL  30000) m2 : NOT(SAL  30000) = (SAL > 30000 26"
    },
    {
      "page_index": 72,
      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_027.png",
      "metadata": {
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        "timestamp": "2025-10-31T13:42:39+07:00"
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      "raw_text": "PHF - Example PAY1 PAY2 TITLE SAL TITLE SAL Mech. Eng 27000 Elect. Eng 40000 Programmer 24000 Syst. Anal. 34000 c 2020,M.T. Ozsu & P.Valduriez 27"
    },
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      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_028.png",
      "metadata": {
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        "page_index": 73,
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      "raw_text": "PHF - Example Fragmentation of relation PROJ 0 Applications: Find the name and budget of projects given their location. 0 Issued at three sites 0 SELECT PNAME, BUDGET 0 FROM PROJ Access project information according to budget WHERE LOC=value one site accesses 200000 other accesses >200000 0 Simple predicates 0 For application (1) P : LOC = \"Montreal' Pz: LOC = \"New York' P3 : LOC = \"Paris' 0 For application (2) P4 : BUDGET  200000 P5 : BUDGET > 200000 0 Pr ={P1,Pz;P3;P4,P5} 0 using COM_MIN Pr'={p1, p2,p4} 0 => minterm predicates M ={m1, m2, m3 , m4, m5, m6} after some minterm predicates eliminated 0 e.g p1 =>not p2 and not p3 ; not p5=> p4 0 loking at database instance some implications may hold: i8: LOC='Montreal' => not(BUDGET >20k) i9: LOC='Paris' => not(Budget <=20k), i10: not(LOC='Montreal')=> BUDGET <=20k,i11: not(LOC='Paris')=> BUDGET > 20k. 0 However, implicatiosn should be defined according to sematics of the database not according to curent values Result of primary hozontal fragmentation of PROJ is to form 6 fragments F ={PROJ1, PROJ2, PROJ3, PROJ4, PROJ5, PROJ6} of PROJ according to M. Since PROJ2 and PROJ5 are empty, they are not depicted 28"
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      "raw_text": "PHF - Example Fragmentation of relation PROJ continued Minterm fragments left after elimination m : (LOC = \"Montreal\") (BUDGET  200000) m, : (LOC = \"Montreal\") (BUDGET > 200000) m3 : (LOC =\"New York\") (BUDGET < 200000 m4 : (LOC = \"New York\") (BUDGET > 200000 m5 : (LOC = \"Paris\") (BUDGET  200000 m6 : (LOC = \"Paris\") (BUDGET > 200000 29"
    },
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        "timestamp": "2025-10-31T13:42:55+07:00"
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      "raw_text": "PHF - Example PR0J1 PNO PNAME BUDGET LOC P1 Instrumentation 150000 Montreal PR0J3 PNO PNAME BUDGET LOC P2 Database Develop 135000 New York PR0J4 PNO PNAME BUDGET LOC P3 CAD/CAM 255000 New York PR0J6 PNO PNAME BUDGET LOC P4 Maintenance 310000 Paris 30"
    },
    {
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      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_031.png",
      "metadata": {
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      "raw_text": "PHF - Correctness Completeness Since Pr' is complete and minimal, the selection predicates are complete Reconstruction If relation R is fragmented into F = {R,,R,,. ..,R,} R = ?Rj FR R Disjointness Minterm predicates that form the basis of fragmentation should be mutually exclusive 31"
    },
    {
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      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_032.png",
      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_032.png",
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      "raw_text": "Derived Horizontal Fragmentation Defined on a member relation of a link according to a selection operation specified on its owner. Each link is an equijoin. Equijoin can be implemented by means of semijoins PAY TITLE, SAL L1 EMP PROJ ENO, ENAME, TITLE PNO, PNAME, BUDGET, LOC L2 ASG ENO, PNO, RESP, DUR 32"
    },
    {
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      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_033.png",
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        "timestamp": "2025-10-31T13:43:05+07:00"
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      "raw_text": "DHF - Definition Given a link L where owner(L)=S and member(L)=R, the derived horizontal fragments of R are defined as Rj=R Xe Sj1<i<w where w is the maximum number of fragments that will be defined on R and Si= f (S) Fi where F; is the formula according to which the primary horizontal fragment S; is defined. 33"
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        "timestamp": "2025-10-31T13:43:11+07:00"
      },
      "raw_text": "DHF - Example Given link L, where owner(L)=SKILL and member(L)=EMP EMP1 = EMP X SKILL where SKILL1= (SKILL) SAL30000 SKILLz= ASG1 ASG2 ENO PNO RESP DUR ENO PNO RESP DUR E3 P3 Consultant 10 E1 P1 Manager 12 E3 P4 Engineer 48 E2 P1 Analyst 24 E4 P2 Programmer 18 E2 P2 Analyst 6 E7 P3 Engineer 36 E5 P2 Manager 24 E6 P4 Manager 48 E8 P3 Manager 40 34"
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    {
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      "chapter_num": 2,
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      "metadata": {
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        "page_index": 80,
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        "timestamp": "2025-10-31T13:43:15+07:00"
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      "raw_text": "DHF - Correctness Completeness Referential integrity  Let R be the member relation of a link whose owner is relation S which is fragmented as Fs = {S,, S,, ..., S,}. Furthermore, let A be the ioin attribute between R and S. Then, for each tuple t of R, there should be a tuple t' of S such that t[A] = t'[A] Reconstruction Same as primary horizontal fragmentation. Disjointness Simple join graphs between the owner and the member fragments. 35"
    },
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      "chapter_num": 2,
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        "page_index": 81,
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        "timestamp": "2025-10-31T13:43:17+07:00"
      },
      "raw_text": "Vertical Fragmentation Has been studied within the centralized context design methodology physical clustering More difficult than horizontal, because more alternatives exist. Two approaches : grouping attributes to fragments splitting relation to fraqments 36"
    },
    {
      "page_index": 82,
      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_037.png",
      "metadata": {
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        "page_index": 82,
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        "timestamp": "2025-10-31T13:43:21+07:00"
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      "raw_text": "Vertical Fragmentation Overlapping fragments grouping Non-overlapping fragments Splitting => focus discussing! We do not consider the replicated key attributes to be overlapping Advantage: Easier to enforce functional dependencies (for integrity checking etc.) 37"
    },
    {
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      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_038.png",
      "metadata": {
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        "timestamp": "2025-10-31T13:43:23+07:00"
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      "raw_text": "VF - Information Requirements Application Information Attribute affinities a measure that indicates how closely related the attributes are This is obtained from more primitive usage data Attribute usage values R[A,A,...,A,] 1 if attribute A, is referenced by query q use(qjAj) = 0 otherwise use(qu.) can be defined accordingly 38"
    },
    {
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      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_039.png",
      "metadata": {
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        "timestamp": "2025-10-31T13:43:28+07:00"
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      "raw_text": "VF - Definition of use(q,Aj) Consider the following 4 queries for relation PROJ q1: SELECT BUDGET 02: SELECT PNAME.BUDGET FROM PROJ FROM PROJ WHERE PNO=Value PNAME 94 93: SELECT SELECT SUM(BUDGET FROM PROJ FROM PROJ WHERE LOC=Value WHERE LOC=Value A1 = PNO.A2=PNAME.A3=BUDGET A4=LOO Attribute Usaqe Matrix: A1 A2 A3 91 1 0 1 0 92 0 1 1 0 93 0 1 0 1 04 0 0 1 39"
    },
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      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_040.png",
      "metadata": {
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        "timestamp": "2025-10-31T13:43:32+07:00"
      },
      "raw_text": "The attribute affinity measure between two attributes A; and A applications aff (A,Aj)=Z (query access) all queries that access A, and A access access frequency of a query query access execution all sites aff(Ai,Aj)= kuse(qk,Ai)=1use(qk,Aj)=1 VSi 40"
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    {
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      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_041.png",
      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_041.png",
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        "timestamp": "2025-10-31T13:43:37+07:00"
      },
      "raw_text": "Assume each guery in the previous example accesses the attributes once during each execution. S. S2 Also assume the access frequencies 91 15 20 10 92 5 0 0 25 25 25 93 3 0 0 94 Then aff(Au A3) PNO PNAME BUDGET LOC = 15*1 + 20*1+10*1 = 45 PNO 0 45 0 and the attribute affinity matrix AA is PNAME 0 5 75 Let A,=PNO,A,=PNAME,A,=BUDGET BUDGET 45 5 3 LOC 0 75 3 A4=LOC) 41"
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      "chapter_num": 2,
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        "timestamp": "2025-10-31T13:43:41+07:00"
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      "raw_text": "VF - Clustering Algorithm Take the attribute affinity matrix AA and reorganize the attribute orders to form clusters where the attributes in each cluster demonstrate high affinity to one another. Bond Energy Algorithm (BEA) has been used for clustering of entities. BEA finds an ordering of entities (in our case attributes) such that the global affinity measure (AM) is maximized AM = affinity of A; and A; with their neighbors) 2 42"
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      "raw_text": "Bond Energy Algorithm Input: The AA matrix Output: The clustered affinity matrix CA which is a perturbation of AA CA. 2 l Iteration: Place the remaining n-i columns in the remaining i+1 positions in the CA matrix. For each column, choose the placement that makes the most contribution to the global affinity measure. ordering 43"
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      "raw_text": "Bond Energy Algorithm \"Best\" placement? Define contribution of a placement: cont(Aj, AkAi) = 2bond(Aj,A)+2bond(Ak A) -2bond(A;, A) where n bond(Ax,Ay) = aff(A,,Ax)aff(Az,Av z =1 44"
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      "raw_text": "BEA - Example Consider the following AA matrix and the corresponding CA matrix where PNO and PNAME have been placed. Pace BUDGET: PNO PNAME BUDGET LOC PNO PNAME PNO 0 45 0 PNO 45 0 PNAME 0 5 75 PNAME 0 80 BUDGET 45 5 3 BUDGET 45 5 LOC 0 75 3 LOC 0 75 Ordering (0-3-1) : COnt(Ao,BUDGET,PNO) = 2bond(Ao, BUDGET)+2bond(BUDGET,PNO) -2bond(A,,PNO) = 8820 Ordering (1-3-2) : C0nt(PNO,BUDGET,PNAME) = 10150 Ordering (2-3-4) : COnt (PNAME,BUDGET,LOC = 1780 45"
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      "raw_text": "BEA - Example Therefore, the CA matrix has the form PNO BUDGET PNAME PNO 45 45 0 PNAME 0 5 80 BUDGET 45 53 5 LOC 0 3 75 When Loc is placed, the nnal torm of the CA matrix (after row organization) is PNO BUDGET PNAME LOC PNO 45 45 0 0 BUDGET 45 53 5 3 PNAME 0 80 75 LOC 0 3 75 78 46"
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      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_047.png",
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      "raw_text": "VF - Algorithm .... A,} into two (or more) sets {A., A,. ..., A} and {A...., A,} such that there are no (or minimal) applications that access both (or more than one) of the sets. A1 A2 A3 ... A; 'Ai+1 An A1 A2 A2 TA Ai Ai+1 BA A 47"
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      "raw_text": "VF - ALgorithm Define TQ set of applications that access only TA - BQ set of applications that access only BA - OQ set of applications that access both TA and BA - and CTQ = total number of accesses to attributes by applications that access only TA CBQ = total number of accesses to attributes by applications that access only BA COQ = total number of accesses to attributes by applications that access both TA and BA Then find the point along the diagonal that maximizes CTQ*CBQ-COQ2 48"
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      "raw_text": "VF - Algorithm Two problems : @ Cluster forming in the middle of the CA matrix Shift a row up and a column left and apply the algorithm to find the \"best\" partitioning point Do this for all possible shifts Cost O(m2) More than two clusters m-way partitioning try 1, 2, ..., m-1 split points along diagonal and try to find the best point for each of these Cost O(2m) 49"
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      "chapter_num": 2,
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      "raw_text": "VF - Correctness A relation R, defined over attribute set A and key K generates the vertical partitioning F, = {R, R,, ..., R,}. Completeness The following should be true for A: A = Reconstruction Reconstruction can be achieved by R=XkR, SR; FR Disiointness TiD's are not considered to be overlapping since they are maintained by the system Duplicated keys are not considered to be overlapping 50"
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        "timestamp": "2025-10-31T13:44:13+07:00"
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      "raw_text": "Hybrid Fragmentation R H H R1 R2 V V V V V R11 R11 R21 R22 R23 51"
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      "chapter_num": 2,
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      "metadata": {
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        "timestamp": "2025-10-31T13:44:15+07:00"
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      "raw_text": "Reconstruction of HF U X X R11 R11 R21 R22 R23 52"
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      "chapter_num": 2,
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      "raw_text": "Outline Distributed and Parallel Database Design Fragmentation Data distribution Combined approaches 53"
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      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_054.png",
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        "timestamp": "2025-10-31T13:44:21+07:00"
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      "raw_text": "Fragment Allocation Problem Statement Given F={F,F...,F.} fragments S ={S,, S,, ..., Sm} network sites Q = {9, q,..., q.}applications Find the \"optimal\" distribution of F to S. Optimality Minimal cost Communication + storage + processing (read & update) Cost in terms of time (usually) Pertormance Response time and/or throughput Constraints  Per site constraints (storage & processing) 54"
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      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_055.png",
      "metadata": {
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        "timestamp": "2025-10-31T13:44:24+07:00"
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      "raw_text": "Information Requirements Database information selectivity of fragments size of a fragment Application information access types and numbers access localities Communication network information unit cost of storing data at a site unit cost of processing at a site Computer system information bandwidth latency communication overhead 55"
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      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_056.png",
      "metadata": {
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      "raw_text": "Allocation File Allocation (FAP) vs Database Allocation (DAP): Fragments are not individual files relationships have to be maintained Access to databases is more complicated remote file access model not applicable relationship between allocation and query processing Cost of integrity enforcement should be considered Cost of concurrency control should be considered 56"
    },
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      "chapter_num": 2,
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        "timestamp": "2025-10-31T13:44:30+07:00"
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      "raw_text": "Allocation Model General Form min(Total Cost) subject to response time constraint storage constraint processing constraint Decision Variable 1 if fragment F, is stored at site S 0 otherwise 57"
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      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_058.png",
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        "timestamp": "2025-10-31T13:44:34+07:00"
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      "raw_text": "Allocation Mode Total Cost query processing cost + all queries cost of storing a fragment at a site all sites all fragments Storage Cost (of fragment F; at Sk) (unit storage cost at Sk) * (size of Fj) * xjk Query Processing Cost (for one query) processing component + transmission component 58"
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    {
      "page_index": 104,
      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_059.png",
      "metadata": {
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        "page_index": 104,
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        "timestamp": "2025-10-31T13:44:36+07:00"
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      "raw_text": "Allocation Mode Query Processing Cost Processing component access cost + integrity enforcement cost + concurrency control cost Access cost (no. of update accesses+ no. of read accesses) * all sites all fragments x j * local processing cost at a site  Integrity enforcement and concurrency control costs  Can be similarly calculated 59"
    },
    {
      "page_index": 105,
      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_060.png",
      "metadata": {
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        "page_index": 105,
        "language": "en",
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        "timestamp": "2025-10-31T13:44:41+07:00"
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      "raw_text": "Allocation Mode Query Processing Cost Transmission component cost of processing updates + cost of processing retrievals Cost of updates update message cost + all sites all fragments acknowledgment cost all sites all fragments Retrieval Cost (cost of retrieval command + ir all sites all fragments cost of sending back the result) 60"
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      "page_index": 106,
      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_061.png",
      "metadata": {
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        "page_index": 106,
        "language": "en",
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      "raw_text": "Allocation Model Constraints  Response Time execution time of query  max. allowable response time for that query  Storage Constraint (for a site) storage requirement of a fragment at that site  storage capacity at that site all fragments  Processing constraint (for a site) processing load of a query at that site  all queries processing capacity of that site 61"
    },
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      "page_index": 107,
      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_062.png",
      "metadata": {
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        "timestamp": "2025-10-31T13:44:46+07:00"
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      "raw_text": "Allocation Mode Solution Methods EAP is NP-complete DAP also NP-complete Heuristics based on single commodity warehouse location (for FAP) knapsack problem branch and bound techniques network flow 62"
    },
    {
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      "chapter_num": 2,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_2/slide_063.png",
      "metadata": {
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      "raw_text": "Allocation Mode Attempts to reduce the solution space assume all candidate partitionings known; select the \"best' partitioning ignore replication at first sliding window on fragments 63"
    },
    {
      "page_index": 109,
      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_001.png",
      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_001.png",
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      "raw_text": "Background - Relational database systems - Computer networks"
    },
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      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_002.png",
      "metadata": {
        "doc_type": "slide",
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_002.png",
        "page_index": 110,
        "language": "en",
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        "timestamp": "2025-10-31T13:44:56+07:00"
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      "raw_text": "Relational Model Relation D,} (not necessarily distinct) with values {Dom1, Dom,, ..., Dom,} is a finite, time varying set of n-tuples (d, d, .., d.> such that d, eDom1, d, e Doma, .., d, e Domn,. Notation: R(A1,Az, ..., An) or R(A1: D1, Az: D2, ..., An: Dn) Alternatively, given R as defined above, an instance of it at a given time is a set of n tuples: {<A1: dy, Az: dz, ...,An: d,> I d e Dom1, d, e Domz, .., dn e Domn} Tabular structure of data where R is the table heading Attributes are table columns Each tuple is a row"
    },
    {
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      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_003.png",
      "metadata": {
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        "page_index": 111,
        "language": "en",
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        "timestamp": "2025-10-31T13:45:03+07:00"
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      "raw_text": "Relation Schemes and Instances Relational scheme - A relation scheme is the definition; i.e., a set of attributes - A relational database scheme is a set of relation schemes: Relation instance  (simply relation) - An relation is an instance of a relation scheme subset of the Cartesian product of the domains of all attributes, i.e.,"
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    {
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      "chapter_num": 3,
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      "metadata": {
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        "page_index": 112,
        "language": "en",
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        "timestamp": "2025-10-31T13:45:08+07:00"
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      "raw_text": "Domains A domain is a type in the programming language sense Name: String Salary: Real Domain values is a set of acceptable values for a variable of a given type. Name: CdnNames ={...}, Salary: ProfSalary = {45,000 - 150,000} Simple/Composite domains Address = Street name+street number+city+province+ postal code Domain compatibility Binary operations (e.g., comparison to one another, addition, etc.) can be performed on them. Full support for domains is not provided in many current relational DBMSs"
    },
    {
      "page_index": 113,
      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_005.png",
      "metadata": {
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        "timestamp": "2025-10-31T13:45:13+07:00"
      },
      "raw_text": "Relation Schemes EMP ENO ENAME TITLE SAL PNO RESP DUR PROJ PNO PNAME BUDGET EMP(ENO, ENAME, TITLE, SAL, PNO, RESP, DUR) PROJ (PNO, PNAME, BUDGET) Underlined attributes are relation keys (tuple identifiers)) Tabular form"
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        "timestamp": "2025-10-31T13:45:25+07:00"
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      "raw_text": "Example Relation Instances EMP ENO ENAME TITLE SAL PNO RESP DUR E1 J. Doe Elect. Eng 40000 P1 Manager 12 E2 M.Smith Analyst 34000 P1 Analyst 24 E2 M.Smith Analyst 34000 P2 Analyst 6 E3 A. Lee Mech. Eng 27000 P3 Consultant 10 E3 A.Lee Mech. Eng. 27000 P4 Engineer 48 E4 J. Miller Programmer 24000 P2 Programmer 18 E5 B. Casey Syst. Anal. 34000 P2 Manager 24 E6 L. Chu Elect.Eng. 40000 P4 Manager 48 E7 R. Davis Mech. Eng 27000 P3 Engineer 36 E8 J. Jones Syst. Anal. 34000 P3 Manager 40 PROJ PNO PNAME BUDGET P1 Instrumentation 150000 P2 Database Develop 135000 P3 CAD/CAM 250000 P4 Maintenance 310000"
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      "raw_text": "Repetition Anomaly The NAME,TITLE, SAL attribute values are repeated for each project that the employee is involved in. - Waste of space 1 Complicates updates EMP ENO ENAME TITLE SAL PNO RESP DUR E1 J. Doe Elect. Eng 40000 P1 Manager 12 E2 M. Smith Analyst 34000 P1 Analyst 24 E2 M. Smith Analyst 34000 P2 Analyst 6 E3 A. Lee Mech. Eng 27000 P3 Consultant 10 E3 A. Lee Mech. Eng 27000 P4 Engineer 48 E4 J. Miller Programmer 24000 P2 Programmer 18 E5 B. Casey Syst. Anal. 34000 P2 Manager 24 E6 L. Chu Elect. Eng 40000 P4 Manager 48 E7 R. Davis Mech. Eng 27000 P3 Engineer 36 E8 J. Jones Syst. Anal. 34000 P3 Manager 40"
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      "raw_text": "Update Anomaly (say SAL of an project employee) is updated, multiple tuples have to be updated to reflect the change. EMP ENO ENAME TITLE SAL PNO RESP DUR E1 J. Doe Elect. Eng. 40000 P1 Manager 12 E2 M. Smith Analyst 34000 P1 Analyst 24 E2 M. Smith Analyst 34000 P2 Analyst 6 E3 A.Lee Mech. Eng 27000 P3 Consultant 10 E3 A. Lee Mech.Eng 27000 P4 Engineer 48 E4 J. Miller Programmer 24000 P2 Programmer 18 E5 B. Casey Syst. Anal. 34000 P2 Manager 24 E6 L. Chu Elect. Eng. 40000 P4 Manager 48 E7 R. Davis Mech. Eng 27000 P3 Engineer 36 E8 J. Jones Syst. Anal. 34000 P3 Manager 40"
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      "raw_text": "Insertion Anomaly It may not be p possible to store information about a new il an employee is project until - assigned to it. EMP ENO ENAME TITLE SAL PNO RESP DUR E1 J. Doe Elect. Eng 40000 P1 Manager 12 E2 M. Smith Analyst 34000 P1 Analyst 24 E2 M. Smith Analyst 34000 P2 Analyst 6 E3 A. Lee Mech. Eng 27000 P3 Consultant 10 E3 A. Lee Mech. Eng 27000 P4 Engineer 48 E4 J. Miller Programmer 24000 P2 Programmer 18 E5 B. Casey Syst. Anal. 34000 P2 Manager 24 E6 L. Chu Elect. Eng 40000 P4 Manager 48 E7 R. Davis Mech. Eng 27000 P3 Engineer 36 E8 J. Jones Syst. Anal. 34000 P3 Manager 40"
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      "raw_text": "Deletion Anomaly If an engineer, who is the only employee on a project, leaves the company, his personal information cannot be deleted, or the information about that project is lost. May have to delete many tuples. EMP ENO ENAME TITLE SAL PNO RESP DUR E1 J. Doe Elect. Eng 40000 P1 Manager 12 E2 M. Smith Analyst 34000 P1 Analyst 24 E2 M. Smith Analyst 34000 P2 Analyst 6 E3 A. Lee Mech. Eng 27000 P3 Consultant 10 E3 A. Lee Mech. Eng 27000 P4 Engineer 48 E4 J. Miller Programmer 24000 P2 Programmer 18 E5 B. Casey Syst. Anal. 34000 P2 Manager 24 E6 L. Chu Elect. Eng 40000 P4 Manager 48 E7 R. Davis Mech. Eng 27000 P3 Engineer 36 E8 J. Jones Syst. Anal. 34000 P3 Manager 40"
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_011.png",
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      "raw_text": "What to do? Take each relation individually and \"improve 'it in terms of the desired characteristics Normal forms Atomic values (1NF) Can be defined according to keys and dependencies. Functional Dependencies ( 2NF, 3NF, BCNF Multivalued dependencies (4NF) Normalization Normalization is a process of concept separation which applies a top-down methodology for producing a schema by subsequent refinements and decompositions. Do not combine unrelated sets of facts in one table; each relation should contain an independent set of facts. Universal relation assumption 1NF to 3NF: 1NF to BCNF"
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      "metadata": {
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      "raw_text": "Normalization  lssues How do we decompose a schema into a desirable normal form? What criteria should the decomposed schemas follow in order to preserve the semantics of the original schema? Reconstructability: recover the original relation => no spurious joins Lossless decomposition: no information loss Dependency preservation: the constraints (i.e., dependencies) that hold on the original relation should be enforceable by means of the constraints (i.e., dependencies) defined on the decomposed relations. What happens to queries? Processing time may increase due to joins Denormalization"
    },
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        "timestamp": "2025-10-31T13:46:21+07:00"
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      "raw_text": "Functional Dependence A,} where X c U, Y c U. If, for all pairs of tuples t 1 relation scheme R, t[[X]= tz[X]=>t1[Y]= tz[Y], then the functional dependency X -> Y holds in R. Example - In relation EMP (ENO, PNO) -> (ENAME, TITLE, SAL, DUR, RESP)"
    },
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      "metadata": {
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      "raw_text": "Normal Forms Based on FDs 1NF eliminates the relations within relations or relations as attributes of tuples First Normal Form (1NF) eliminate the partial functional dependencies of non-prime attributes to key attributes Lossless & Second Normal Form n (2NF) Dependency eliminate the transitive functional dependencies of non-prime attributes to key attributes Third Normal Form (3NF) Lossless eliminate the partial and transitive functional dependencies of prime (key) attributes to key. Boyce-Codd Normal Form (BCNF)"
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      "metadata": {
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        "timestamp": "2025-10-31T13:46:38+07:00"
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      "raw_text": "Normalized Relations s - Example EMP ASG ENO ENAME TITLE ENO PNO RESP DUR E1 J. Doe Elect. Eng E1 P1 Manager 12 E2 M. Smith Syst. Anal. E2 P1 Analyst 24 E3 A.Lee Mech. Eng E2 P2 Analyst 6 E4 J. Miller Programmer E3 P3 Consultant 10 E5 B. Casey Syst. Anal. E3 P4 Engineer 48 E6 L. Chu Elect. Eng E4 P2 Programmer 18 E7 R.Davis Mech. Eng E5 P2 Manager 24 E8 J. Jones Syst. Anal. E6 P4 Manager 48 E7 P3 Engineer 36 E8 P3 Manager 40 PROJ PAY TITLE SAL PNO PNAME BUDGET P1 Instrumentation 150000 Elect. Eng 40000 P2 Database Develop. 135000 Syst. Anal. 34000 P3 CAD/CAM 250000 Mech. Eng 27000 P4 Maintenance 310000 Programmer 24000"
    },
    {
      "page_index": 124,
      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_016.png",
      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_016.png",
        "page_index": 124,
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        "timestamp": "2025-10-31T13:46:43+07:00"
      },
      "raw_text": "Relational Algebra Specify how to obtain the result using a set of operators Form (Operands) -> (Result Relation (s) Relation"
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      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_017.png",
      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_017.png",
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        "timestamp": "2025-10-31T13:46:48+07:00"
      },
      "raw_text": "Relational Algebra Operators Fundamental Selection Projection - Union Set difference Cartesian product Additional Intersection 0-join Natural joir Semijoin Division Union compatibility Same degree Corresponding attributes defined over the same domain"
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      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_018.png",
      "metadata": {
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        "timestamp": "2025-10-31T13:46:52+07:00"
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      "raw_text": "Selection Produces a horizontal subset of the operand relation General form oF(R)={ttcR and F(t) is true} where - R is a relation, t is a tuple variable - F is a formula consisting of arithmetic comparison operators <,>,=,,<,"
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    {
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      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_019.png",
      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_019.png",
        "page_index": 127,
        "language": "en",
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        "timestamp": "2025-10-31T13:46:58+07:00"
      },
      "raw_text": "Selection E xample EMP ENO ENAME TITLE E1 J. Doe Elect. Eng (EMP) E2 OTITLE='Elect. Eng.' M. Smith Syst. Anal. E3 A. Lee Mech. Eng ENO ENAME TITLE E4 J. Miller Programmer E1 J. Doe Elect. Eng E5 B. Casey Syst. Anal. 1 E6 L. Chu Elect. Eng. E6 L. Chu Elect. Eng E7 R. Davis Mech. Eng E8 J. Jones Syst. Anal."
    },
    {
      "page_index": 128,
      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_020.png",
      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_020.png",
        "page_index": 128,
        "language": "en",
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        "timestamp": "2025-10-31T13:47:04+07:00"
      },
      "raw_text": "P r rojection Produces a vertical slice of a relation General form (R)={t[A1,..,A,] tER} h where R is a relation, t is a tuple variable - 1 will be performed Note: projection can generate duplicate tuples. Commercial systems (and SQL) allow this and provide Projection with duplicate elimination Projection without duplicate elimination"
    },
    {
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      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_021.png",
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        "page_index": 129,
        "language": "en",
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        "extractor_version": "1.0.0",
        "timestamp": "2025-10-31T13:47:10+07:00"
      },
      "raw_text": "P r rojection E :xample PROJ (PROJ) PNO PNAME BUDGET PNO BUDGET P1 Instrumentation 150000 P1 150000 P2 Database Develop. 135000 P2 135000 P3 CAD/CAM 250000 P3 250000 P4 Maintenance 310000 P4 310000"
    },
    {
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      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_022.png",
      "metadata": {
        "doc_type": "slide",
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_022.png",
        "page_index": 130,
        "language": "en",
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        "timestamp": "2025-10-31T13:47:15+07:00"
      },
      "raw_text": "Union Similar to set union General form R U S={t  R, S are relations, t is a tuple variable where - Result contains tuples that are in R or in S, but not both (duplicates removed) - R, S should be union-compatible"
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    {
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      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_023.png",
      "metadata": {
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        "page_index": 131,
        "language": "en",
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        "timestamp": "2025-10-31T13:47:19+07:00"
      },
      "raw_text": "Set Difference General Form R-S={tt eR and t S} where R and S are relations, t is a tuple variable - Result contains all tuples that are in R, but not in S. - R - S X S - R - R, S union-compatible"
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        "timestamp": "2025-10-31T13:47:25+07:00"
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      "raw_text": "Cartesian C Product ross Given relations - R of degree k1. cardinality n1 - S of degree k,, cardinality n2 Cartesian (cross) product: R x S ={t [A1,...,Ak ] t[A1,...,Ak 1 E R and 2 S} E The result of R . S is a relation of degree (k+ k,) and consists of all (n1* n,)-tuples where each tuple is a concatenation of one tuple of R with one tuple of S."
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        "timestamp": "2025-10-31T13:47:40+07:00"
      },
      "raw_text": "Cartesian roduct E :xample EMP x PAY EMP ENO ENAME TITLE ENO ENAME EMP TITLE PAY.TITLE SALARY E1 J. Doe Elect. Eng E1 J. Doe Elect. Eng. Elect. Eng. 55000 E2 M. Smith Syst. Anal. E1 J. Doe Elect. Eng. Syst. Anal. 70000 E3 A. Lee Mech. Eng E1 J. Doe Elect. Eng. Mech. Eng 45000 E4 J. Miller Programmer E1 J. Doe Elect. Eng. Programmer 60000 E5 B. Casey Syst. Anal. E2 M. Smith Syst. Anal. Elect. Eng. 55000 E6 L. Chu Elect. Eng E2 M. Smith Syst. Anal. Syst. Anal. 70000 E7 R. Davis Mech. Eng E2 M. Smith Syst. Anal. Mech. Eng 45000 E8 J. Jones Syst. Anal E2 M. Smith Syst. Anal. Programmer 60000 E3 A. Lee Mech. Eng. Elect. Eng. 55000 1 E3 A. Lee Mech. Eng.Syst. Anal. 70000 PAY E3 A. Lee Mech. Eng.Mech. Eng 45000 TITLE SALARY E3 A. Lee Mech. Eng.Programmer 60000 Elect. Eng 55000 E8 J. Jones Syst. Anal. Elect. Eng. 55000 Syst. Anal. 70000 E8 J. Jones Syst. Anal. Syst. Anal. 70000 Mech. Eng 45000 E8 J. Jones Syst. Anal. Mech. Eng 45000 Programmer 60000 E8 J. Jones Syst. Anal. Programmer 60000"
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      "raw_text": "Intersection Typical set intersection ={tt E Rand t E S} R nS =R-(R-S) R, S union-compatible"
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    {
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        "timestamp": "2025-10-31T13:47:49+07:00"
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      "raw_text": "0-Join General form R S={t[A B ,...,Ap F(R.Ai,S.Bj) n/ t[A R and t[B S A 1,...,B - E E n and F(R.A 4i, S.B;) is true} where - R, S are relations, t is a tuple variable - F (R.A, S.B;)is a formula defined as that of selection. A derivative of Cartesian product"
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      "raw_text": "Join E xample EMP X ASG EMP EMP.ENO=ASG.ENO ENO ENAME TITLE ENO ENAME TITLE PNO RESP DUR E1 J. Doe Elect. Eng E1 J.Doe Elect. Eng P1 Manager 12 E2 M. Smith Syst. Anal. E2 M. Smith Syst. Anal. P1 Analyst 12 E3 A. Lee Mech.Eng E2 M. Smith Syst. Anal. P2 Analyst 12 E4 J. Miller Programmer E3 A. Lee Mech. Eng. P3 Consultant 12 E5 B. Casey Syst. Anal. E3 A. Lee Mech. Eng P4 Engineer 12 E6 L. Chu Elect. Eng E4 J. Miller Programmer P2 Programmer 12 E7 R. Davis Mech. Eng E5 J. Miller Syst. Anal. P2 Manager 12 E8 J. Jones E6 L. Chu Elect. Eng. Syst. Anal. P4 Manager 12 E9 A. Hsu Programmer E7 R. Davis Mech. Eng P3 Engineer 12 E10 T. Wong Syst. Anal. E8 J. Jones Syst. Anal. P3 Manager 12 (a) (b)"
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      "raw_text": "Types s of Join Equi-join - The formula F only contains equality R X S R.A=S.B Natural join Equi-join of two relations R and S over an attribute (or attributes) common to both R and S and projecting out one copy of those attributes"
    },
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      "raw_text": "Example Natural Join EMP ENO ENAME TITLE EMP X PAY E1 J. Doe Elect. Eng E2 M. Smith Syst. Anal. ENO ENAME TITLE SALARY E3 A. Lee Mech. Eng E4 J. Miller Programmer E1 J. Doe Elect. Eng 55000 E5 B. Casey Syst. Anal. E2 M. Smith Analyst 70000 E6 L. Chu Elect. Eng E3 A. Lee Mech. Eng 45000 E7 R. Davis Mech. Eng E8 J. Jones Syst. Anal. E4 J. Miller Programmer 60000 E5 B. Casey Syst. Anal. 70000 PAY E6 L. Chu Elect. Eng 55000 TITLE SALARY E7 R. Davis Mech. Eng 45000 E8 J. Jones Syst. Anal. 70000 Elect. Eng 55000 Syst. Anal. 70000 Mech. Eng 45000 Join is over the common attribute  TITLE Programmer 60000"
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      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_031.png",
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        "page_index": 139,
        "language": "en",
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      "raw_text": "Types s of Join Outer-Join - Ensures that tuples from one or both relations that do not satisfy the join condition still appear in the final result with other relation' s attribute values set>to NULL - Left outer jin - Right outer join - Full outer join"
    },
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      "chapter_num": 3,
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        "timestamp": "2025-10-31T13:48:32+07:00"
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      "raw_text": "Example Outer J Join Left outer join EMPTXA ASG ENO ENO ENAME TITLE PNO RESP DUR E1 J.Doe Elect. Eng. P1 Manager 12 E2 M. Smith Syst. Anal. P1 Analyst 12 E2 M. Smith Syst. Anal. P2 Analyst 12 E3 A. Lee Mech. Eng P3 Consultant 12 E3 A. Lee Mech. Eng P4 Engineer 12 E4 J. Miller Programmer P2 Programmer 12 E5 J.Miller Syst. Anal. P2 Manager 12 E6 L. Chu Elect. Eng. P4 Manager 12 E7 R. Davis Mech. Eng P3 Engineer 12 E8 J. Jones Syst. Anal. P3 Manager 12 E9 A.Hsu Programmer Null Null Null E10 T.Wong Syst. Anal. Null Null Null"
    },
    {
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      "chapter_num": 3,
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        "timestamp": "2025-10-31T13:48:37+07:00"
      },
      "raw_text": "Semijoin Derivation S =IIA(R s) R X  =IIA(R) 1 AnB(S) = R X X X F F F 1 AnB(S) where - R, S are relations - A is a set of attributes"
    },
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      "chapter_num": 3,
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        "language": "en",
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        "extractor_version": "1.0.0",
        "timestamp": "2025-10-31T13:48:43+07:00"
      },
      "raw_text": "Semijoin Example EMP X PAY EMP.TITLE=PAY.TITLE ENO ENAME TITLE E1 J. Doe Elect. Eng E2 M. Smith Analyst E3 A. Lee Mech. Eng. E4 J.Miller Programmer E5 B.Casey Syst. Anal. E6 L. Chu Elect. Eng E7 R. Davis Mech. Eng E8 J. Jones Syst. Anal."
    },
    {
      "page_index": 143,
      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_035.png",
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        "page_index": 143,
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      "raw_text": "Division (Quotient) Given relations -R of degree k1(R ={A1 } 1,...,A k. - S of degree k, (S = {B B1,...,Bk 2 {A1,..A Let A = K } [i.e.,R(A)] S(B)] and B cA. Then, T = R - S gives T of degree k-k, [i.e., T(Y) where Y = A-B] such that for a tuple t to appear in T, the values in t must appear in R in combination with every tuple in S. Derivation"
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      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_036.png",
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        "timestamp": "2025-10-31T13:48:57+07:00"
      },
      "raw_text": "Example Division PROJ ASG ENO PNO PNAME BUDGET PNO PNAME BUDGET E1 P1 Instrumentation 150000 P3 CAD/CAM 250000 E2 P1 Instrumentation 150000 P4 Maintenance 310000 E2 P2 Database Develop. 135000 E3 P3 CAD/CAM 250000 E3 P4 Maintenance 310000 E4 P2 Database Develop. 135000 (ASG'  PROJ') E5 P2 Database Develop. 135000 E6 P4 Maintenance 310000 ENO E7 P3 CAD/CAM 250000 E3 E8 P3 CAD/CAM 250000"
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      "page_index": 145,
      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_037.png",
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      "raw_text": "Relational Calculus Specify the properties that the result should hold Tuple relational calculus Domain relational calculus"
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      "raw_text": "Tuple Relational Calculus Query of the form {tF{t}} where 3 - t is a tuple variable - F is a well-formed formula Atomic formula - Tuple-variable membership expressions R.t or R(t) : tuple t belongs to relation R - Conditions s[A] 0 t[B]; s and t are tuple variables, A and B are components of s and t, respectively,  e {<,>, =,+, , -}; e.g.,s[SAL] > t[SAL] s[A]  c; s, A, and  as defined above, c is a constant;"
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        "timestamp": "2025-10-31T13:49:12+07:00"
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      "raw_text": "Relational Calculus Domain I where the fre EMP ENO ENAME TITLE E2 P. QBE is ar ASG ENO PNO RESP DUR E2 P3 PROJ PNO PNAME BUDGET P3 CAD/CAM"
    },
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      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_040.png",
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        "timestamp": "2025-10-31T13:49:16+07:00"
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      "raw_text": "Computer Network Switches An / interconnected collection of autonomous Hosts computers that are capable of exchanging information Network among themselves."
    },
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      "page_index": 149,
      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_041.png",
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        "timestamp": "2025-10-31T13:49:21+07:00"
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      "raw_text": "Internet Network of networks Intranet Client R Intranet R ISP Network R Intranet R Server R ISP Network"
    },
    {
      "page_index": 150,
      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_042.png",
      "metadata": {
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        "timestamp": "2025-10-31T13:49:26+07:00"
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      "raw_text": "Types s of Networks According to scale (geographic distribution) - Wide are network (WAN) high as thousands of kms Long delays due to distance traveled Heterogeneity of transmission media Speeds of 150Mbps to 10Gbps (OC192 on the backbone) - Local area network (LAN) Limited in geographic scope (usually < 2km) Speeds 10-1000 Mbps"
    },
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      "page_index": 151,
      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_043.png",
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      "raw_text": "Types s of Networks (cont'd) Topology -Irregular No regularity in the interconnection - e.g., Internet - Bus Typical in LANs - Ethernet Using Carrier Sense Medium Access with Collision Detection (CSMA/CD) - Listen before and while you transmit - Star - Ring -Mesh"
    },
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      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_044.png",
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        "timestamp": "2025-10-31T13:49:35+07:00"
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      "raw_text": "Bus network Host Host #1 #3 Bus Interface B U S Host Host #2 #4"
    },
    {
      "page_index": 153,
      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_045.png",
      "metadata": {
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        "page_index": 153,
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        "timestamp": "2025-10-31T13:49:42+07:00"
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      "raw_text": "Communication Schemes Point-to-point : (unicast) - One or more (direct or indirect) links between each pair of nodes - Communication always between two nodes - Receiver and sender are identified by their addresses included in the message header - Message may follow one of many links between the sender and receiver using switching or routing Broadcast  (multi-point) - Messages are transmitted over a shared channel ivod bv all tbo"
    },
    {
      "page_index": 154,
      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_046.png",
      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_046.png",
        "page_index": 154,
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        "timestamp": "2025-10-31T13:49:45+07:00"
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      "raw_text": "Communication Alternatives Twisted pair Coaxial Fiber optic cable Satellite Microwave Wireless"
    },
    {
      "page_index": 155,
      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_047.png",
      "metadata": {
        "doc_type": "slide",
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_047.png",
        "page_index": 155,
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        "timestamp": "2025-10-31T13:49:51+07:00"
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      "raw_text": "Data Communication Hosts are connected by links, each of which can carry one or more channels Link: physical entity; channel: logical entity Digital signal versus analog signal Capacity - bandwidth - The amount of information that can be trnsmitted over the channel in a given time unit - Packet switching Messages divided into fixed size packets, each of which  are"
    },
    {
      "page_index": 156,
      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_048.png",
      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_048.png",
        "page_index": 156,
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        "timestamp": "2025-10-31T13:49:55+07:00"
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      "raw_text": "Packet Format Block Error Header Text Check Source address Destination address Message number - Packet number - Acknowledgment  Control information"
    },
    {
      "page_index": 157,
      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_049.png",
      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_049.png",
        "page_index": 157,
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        "timestamp": "2025-10-31T13:49:59+07:00"
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      "raw_text": "Communication Protocols Software that ensures error-free, reliable and efficient communication between hosts Layered architecture - hence protocol stack protocol suite or r TCP/IP is the best-known one - Used in the Internet"
    },
    {
      "page_index": 158,
      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_050.png",
      "metadata": {
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        "timestamp": "2025-10-31T13:50:04+07:00"
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      "raw_text": "Transmission using Message TCP/IP Message Message Application Layer Application Layer Transport Layer Transport Layer Internet Layer Internet Layer Local Local Network Network"
    },
    {
      "page_index": 159,
      "chapter_num": 3,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_3/slide_051.png",
      "metadata": {
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        "timestamp": "2025-10-31T13:50:08+07:00"
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      "raw_text": "TCP/IP P r rotocol Application HTML, HTTP, FTP Telnet NFS SNMP Transport TCP UDP IP Network Individual WiFi Ethernet Token Ring ATM FDDI Networks"
    },
    {
      "page_index": 160,
      "chapter_num": 4,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_4/slide_001.png",
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        "timestamp": "2025-10-31T13:50:13+07:00"
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      "raw_text": "Processing Query D-DBMS In Lecturer: Nguyen Dinh Thanh Email: dinhthanh@hcmut.edu.vn More info: https://nguyendinhthanh.net DAI HOC QUOC GIA THANH PHO HO CHi MINH TRU'O'NG DAI HOC BACH KHOA"
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      "page_index": 161,
      "chapter_num": 4,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_4/slide_002.png",
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        "timestamp": "2025-10-31T13:50:18+07:00"
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      "raw_text": "Query Processing in a DDBMS high level user r query query processor Low-level data manipulation commands for D-DBMS 2"
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      "page_index": 162,
      "chapter_num": 4,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_4/slide_003.png",
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        "timestamp": "2025-10-31T13:50:23+07:00"
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      "raw_text": "Processing Query Components Query language that is used SQL: \"intergalactic dataspeak' Query execution methodology The steps that one goes through in executing high-level (declarative) user queries Query optimization How do we determine the \"best\" execution plan? 3"
    },
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      "raw_text": "Selecting Alternatives EMP (ENO. ENAME TITLE ASG (ENO: PNO, RESP. DUR) \"Find the of employees who names are managing a project\" SELECT ENAME FROM EMP, ASG WHERE EMP.ENO ASG.ENO AND RESP Strategy 1 1 (EMPxASG)) (0 ENAME RESP=\"Manager\"EMP.ENO=ASG.ENO Strategy 2 H EMP (0 (ASG)) X ENAME ENO RESP=\"Manager\" Strategy 2 avoids Cartesian product, so may be \"better\" 4"
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      "raw_text": "What is the Problem? 11 EMP (0 (ASG)) X ENAME ENO RESP=\"Manager\" Site 1 Site 2 Site 3 Site 4 Site 5 Result ASG= 3\"(ASG) EMP (EMP) EMP 0 (EMP) ENO<\"E3 2 ENO>\"E3\"l Execution Strategy 1 Execution Strategy 2 Site 5 Site 5 result =EMP'UEMP 2 result=(EMP,EMP X ASG,x ASG ENO RESP=\"Manager\" 2 EMP EMP 2 Site 3 Site 4 ASG ASG EMP EMP 2 2 EMP' =EMP ASG EMP'.=EMP ASG X X ENO 2 2 ENO 2 Site 1 Site 2 Site 3 Site 4 ASG ASG Site 1 Site 2 ASG1=0RESP=\"Manager\"F ASG ASG'2 = 0 RESP=\"Manager\" ASG 5 Which is better? Strategy 1 or Strategy 2"
    },
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      "chapter_num": 4,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_4/slide_006.png",
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      "raw_text": "Cost of Alternatives Assume size(EMP) = 400,size(ASG) = 1000 tuple access cost = 1 unit; tuple transfer cost = 10 units 20 managers in ASG relation Data is uniformly distributed among sites Strategy 1 produce AsG': (10+10) * tuple access cost 20 transfer ASG' to the sites of EMP: (10+10) * tuple transfer cost 200 produce EMP': (10+10) * tuple access cost * 2 40 transfer EMp' to result site: (10+10) * tuple transfer cost 200 Total Cost 460 Strategy 2 transfer EMP to site 5: 400  * tuple transfer cost 4,000 transfer ASG to site 5: 1000 * tuple transfer cost 10,000 produce ASG': 1000 * tuple access cost 1,000 join EMP and ASG': 400 20  * tuple access cost 8,000 Total Cost 23,000 6"
    },
    {
      "page_index": 166,
      "chapter_num": 4,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_4/slide_007.png",
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        "page_index": 166,
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      "raw_text": "Query  Optimization n Objectives Minimize a cost function I/O cost + CPU cost + communication cost These might have different weights in different distributed environments Wide area networks communication cost may dominate or vary much bandwidth speed high protocol overhead Local area networks communication cost not that dominant total cost function should be considered Can also maximize throughput 7"
    },
    {
      "page_index": 167,
      "chapter_num": 4,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_4/slide_008.png",
      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_4/slide_008.png",
        "page_index": 167,
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        "timestamp": "2025-10-31T13:51:02+07:00"
      },
      "raw_text": "Complexity of Relational Operations Operation Complexity Select Project O(n) Assume (without duplicate elimination) relations of cardinality n Project (with duplicate elimination) O(n * 1og n) sequential scan Group Join Semi-join O(n * 1og n) Division Set Operators O(n2) Cartesian Product 8"
    },
    {
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      "chapter_num": 4,
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      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_4/slide_009.png",
        "page_index": 168,
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        "timestamp": "2025-10-31T13:51:08+07:00"
      },
      "raw_text": " Issues - Types Query  Optimization Of Optimizers Exhaustive search Cost-based Optimal Combinatorial complexity in the number of relations Heuristics Not optimal Regroup common sub-expressions Perform selection, projection first Replace a join by a series of semijoins Reorder operations to reduce intermediate relation size Optimize individual operations 9"
    },
    {
      "page_index": 169,
      "chapter_num": 4,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_4/slide_010.png",
      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_4/slide_010.png",
        "page_index": 169,
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        "timestamp": "2025-10-31T13:51:13+07:00"
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      "raw_text": "Query Optimization Issues -  Granularity Optimization Single query at a time Cannot use common intermediate results Multiple queries at a time Efficient if many similar queries Decision space is much larger 10"
    },
    {
      "page_index": 170,
      "chapter_num": 4,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_4/slide_011.png",
      "metadata": {
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        "page_index": 170,
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      "raw_text": "Query  Optimization Issues - Optimization Timing Static Compilation  optimize prior to the execution Difficult to estimate the size of the intermediate results = error propagation Can amortize over many executions Dynamic Run time optimization Exact information on the intermediate relation sizes Have to reoptimize for multiple executions Distributed lNGRES Hybrid Compile using a static algorithm If the error in estimate sizes > threshold, reoptimize at run time Mermaid 11"
    },
    {
      "page_index": 171,
      "chapter_num": 4,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_4/slide_012.png",
      "metadata": {
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      "raw_text": "Query  Optimization Issues - Statistics Relation Cardinality Size of a tuple Fraction of tuples participating in a join with another relation Attribute Cardinality of domain Actual number of distinct values Common assumptions Independence between different attribute values Uniform distribution of attribute values within their domain 12"
    },
    {
      "page_index": 172,
      "chapter_num": 4,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_4/slide_013.png",
      "metadata": {
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        "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_4/slide_013.png",
        "page_index": 172,
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      "raw_text": "Query Optimization Issues - Decision Sites Centralized Single site determines the \"best\" schedule Simple Need knowledge about the entire distributed database Distributed Cooperation among sites to determine the schedule Need only local information Cost of cooperation Hybrid One site determines the global schedule Each site optimizes the local subqueries 13"
    },
    {
      "page_index": 173,
      "chapter_num": 4,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_4/slide_014.png",
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        "page_index": 173,
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        "timestamp": "2025-10-31T13:51:37+07:00"
      },
      "raw_text": "Query Optimization i Issues - Network Topology Wide area networks (WAN) - point-to-point Characteristics Low bandwidth Low speed High protocol overhead Communication cost will dominate; ignore all other cost factors Global schedule to minimize communication cost Local schedules according to centralized query optimization Local area networks (LAN) Communication cost not that dominant Total cost function should be considered Broadcasting can be exploited (joins) Special algorithms exist for star networks 14"
    },
    {
      "page_index": 174,
      "chapter_num": 4,
      "source_file": "/workspace/data/converted/CO3023_Distributed_and_Object-Oriented_Databases/Chapter_4/slide_015.png",
      "metadata": {
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        "page_index": 174,
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        "timestamp": "2025-10-31T13:51:44+07:00"
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      "raw_text": "Distributed Query Processing Methodology Calculus Query on Distributed Relations Query GLOBAL Decomposition SCHEMA Algebraic  Ouery on Distributed Relations CONTROL FRAGMEN Data SITE T Localization SCHEMA Fragment Query STATS ON Global FRAGMEN Optimization TS Optimized Fragment Query with Communication Operations Local LOCAL LOCAL Optimization SCHEMAS SITES Optimized Local Queries 15"
    }
  ]
}