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ITDataset.txt

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+Challenges in Ubiquitous Data Management 
+Improved hardware and networking are clearly central to the development of 
+ubiquitous computing, but an equally important and difficult set of challenges revolve 
+around Data Management [AK93]. In order for computing to fade into the 
+background while supporting more and more activities, the data required to support 
+those activities must be reliably and efficiently stored, queried, and delivered. 
+Traditional approaches to data management such as caching, concurrency control, 
+query processing, etc. need to be adapted to the requirements and restrictions of 
+ubiquitous computing environments. These include resource limitations, varying and 
+intermittent connectivity, mobile users, and dynamic collaborations. 
+
+In this paper we first discuss the main characteristics of applications that 
+ubiquitous computing aims to support and then focus on the requirements that such 
+applications impose on data management technology. We then examine several 
+different aspects of data management and how they are being adapted to these new 
+requirements. 
+
+Applications and Data Management Requirements 
+
+While there is wide agreement on the great potential of ubiquitous computing, it is not 
+yet clear what the killer applications (i.e., the uses that will result in widespread 
+adoption) will be. Many researchers and product developers have created example 
+scenarios to demonstrate the potential of the technology. Due to the integrated and 
+universal nature of ubiquitous computing, these scenarios tend to include a large 
+number of functions rather than any one single application. Thus, some in industry 
+have begun to talk in terms of delivering a certain type of user experience rather 
+than a particular application or suite of applications. These scenarios tend to involve 
+users with several portable devices, moving between different environments (e.g., 
+home, car, office, conference). The devices typically take an active (and often 
+annoying) role in reminding the user of various appointments and tasks that are due, 
+provide access to any and all information that may be relevant to these tasks, and 
+facilitate communication among groups of individuals involved in the tasks. 
+
+Categories of Functionality 
+
+Rather than specify yet another such scenario, it is perhaps more useful to categorize 
+the functionalities that such scenarios imply. This categorization can then be 
+examined to determine the requirements that are imposed on data management. The 
+functionalities can be classified into the following: 
+
+1) Support for mobility the compactness of the devices combined with 
+wireless communication means that the devices can be used in mobile 
+situations. Thus, existing applications must be able to operate in varied 
+and dynamic communication and computation environments, possibly moving from one network or service provider to another. Furthermore, 
+new applications that are location-centric will also be developed. 
+
+2) Context awareness if devices become truly ubiquitous, then they will 
+be used constantly in a wide range of continually changing situations. 
+For the devices to be truly helpful, they must be aware of the 
+environment as well as the tasks that the user is performing or will be 
+performing in the near future. Context aware applications range from 
+intelligent notification systems that inform the user of (hopefully) 
+important events or data, to smart spaces , that is, rooms or 
+environments that adapt based on who is present and what they are 
+doing. 
+
+3) Support for collaboration another key theme of ubiquitous computing 
+applications is the support of groups of people. This support consists of 
+communications and conferencing as well as the storage, maintenance, 
+delivery, and presentation of shared data. Collaborations may be 
+performed in real-time, if all of the participants are available, or may be 
+done asynchronously otherwise. In addition to supporting on-going 
+collaboration, access to and analysis of traces of past activities is also 
+required. 
+
+Adaptivity and User Interaction 
+
+These functionalities provide a host of challenges for data management techniques, 
+but one requirement is present across all of them, namely, the need for adaptivity. 
+Mobile users and devices, changing contexts, and dynamic groups all impose 
+requirements for flexibility and responsiveness that are simply not addressed by most 
+traditional data management techniques. Thus, adaptivity is a common theme of the 
+techniques that we discuss in the remainder of the paper. 
+
+It is also important to note that because ubiquitous computing is intended to 
+augment human capabilities in the execution of various tasks, the nature of these 
+applications is that the user is typically interacting in real-time with the computers. 
+We are able to exploit this fact as part of the solution to adaptivity by, in some cases, 
+depending on the users to make dynamic choices or to cope with some degree of 
+ambiguity. A concrete example of such a design choice is the way that many 
+groupware systems handle concurrent access and update to shared data. Rather than 
+impose rules that restrict the types and degrees of interaction that users can have, as is 
+done by concurrency control mechanisms in traditional database systems, a 
+groupware data manager will typically impose less stringent rules. The relaxation of 
+these rules limits the extent to which the system can autonomously handle conflicts. 
+Thus, such systems typically handle whatever cases they can, and when they detect a 
+conflict that cannot be handled automatically, they simply inform the user(s) that the 
+conflict has occurred, and allow them to resolve it based on their knowledge of the 
+situation. Thus, having users in the loop can be leveraged to provide more adaptive 
+and flexible systems. 
+
+Challenges in Ubiquitous Data Management 
+
+Requirements Due to Mobility 
+
+Other data management requirements are less universal across the three categories but 
+yet must be addressed in order to support a comprehensive ubiquitous computing 
+environment. For example, the issue of mobility raises a number of issues. First, the 
+fact that the terminals (i.e. devices) are constantly moving, and often have limited 
+storage capacity means that a ubiquitous computing system must be able to deliver 
+data to and receive data from different and changing locations. This results in the 
+need for various kinds of proxy solutions, where users are handed off from one proxy 
+to another as they move. Protocols must be constructed in such a way as to be able to 
+tolerate such handoffs without breaking. Mobility also raises the need for intelligent 
+data staging and pre-staging, so that data can be placed close to where the users will 
+be when they need it (particularly in slow or unreliable communications situations). 
+
+Secondly, mobility adds location as a new dimension to applications that does not 
+typically play a role in stationary scenarios. For example, some of the most useful 
+applications for mobile devices are location-centric. Consider a system that can 
+answer questions such as find the drugstores within 2 miles of my current location . 
+Such a system must track the location of the current user and be able to access 
+information based on relative locations and distances. On a broader scale, servers 
+may have to track large numbers of moving objects (people, cars, devices, etc.) and be 
+able to predict their future locations. For example, an automated traffic control 
+system would have to track numerous cars, including their current positions, 
+directions, and velocities. Location-centric computing requires special data structures 
+in which location information can be encoded and efficiently stored, as well as ones in 
+which the dynamic positions of objects can be maintained. 
+
+Requirements Due to Context-Awareness 
+
+Context-awareness imposes significant demands on the knowledge maintained by the 
+system and the inferencing algorithms that use that knowledge. In order to be context 
+aware, a system must maintain an internal representation of users needs, roles, and 
+preferences, etc. One example of such a system is a smart calendar that routes 
+information to a user based on knowledge of the user s near-term schedule (as can be 
+determined from the user s PIM calendar). If, for example, a user has a meeting with 
+a particular client scheduled for the afternoon, such a system could send the user 
+information that would be highly relevant to that meeting, such as data about the 
+client s account, results of previous meetings with that client, news articles relevant to 
+the topic of the meeting, etc. 
+
+More sophisticated systems might use various types of sensors to monitor the 
+environment and track users actions so as to be able to assist in the tasks the user is 
+performing. Such sensor-based systems require the ability to process data streams in 
+real-time and to analyze and interpret such streams. Thus, data-flow processing will 
+play a key role in ubiquitous computing. 
+
+Whether the system obtains its context information from sensors, user input, PIM 
+(personal information management) applications, or some combination of these, it 
+must perform a good deal of processing over the data in order to be able to accurately 
+assess the state of the environment and the intensions of the user. Thus, context- 
+aware applications impose demanding requirements for inferencing and machine 
+learning techniques. These processes will have to cope with incomplete and 
+conflicting data, and will have to do so extremely efficiently in order to be able to 
+interact with the user in a useful and unobtrusive manner. 
+
+Requirements Due to Collaboration 
+
+The final set of requirements we discuss here are those that result from the need to 
+support collaborative work by dynamic and sometimes ad hoc groups of people. As 
+stated above, a prime requirement that stems from such applications is adaptivity. In 
+addition, however, there are several other types of support that are required beyond 
+what has already been discussed. First, there is a need for synchronization and 
+consistency support. At the center of any collaborative application is a set of shared 
+data items that ean be created, accessed, modified, and deleted by participants of the 
+collaboration. This coordination function must be lightweight and flexible so that 
+many different types of interactions can be supported, ranging from unmoderated chat 
+facilities, to full ACID (Atomic, Consistent, Isolated, and Durable) transactions, as 
+provided by traditional database systems. 
+
+A second requirement stemming from collaborative applications is the need for 
+reliable and available storage of history. In particular, if the collaboration is to be 
+performed in an asynchronous manner, users must be able to access a record of what 
+has happened earlier in the collaboration. Likewise, if the participants in the 
+collaboration can change over time (e.g., due to mobility, failures, or simply due to 
+the nature of the collaboration), then a durable record of participants and their actions 
+is essential to allow new members to join and come up to speed. Such durable storage 
+is also useful for keeping a log of activity, that can be used later to trace through the 
+causes of various outcomes of the collaboration, or as input into learning and data 
+mining algorithms whieh may help optimize future collaborations. 
+
+Example Data Management Technologies n On-Going Projects 
+
+The preceding discussion addressed some of the data management challenges that 
+must be addressed to support ubiquitous computing scenarios and outlined the 
+application properties from which they arise. In this section, we briefly describe two 
+on going projects that are addressing some of these aspects. The first project, called 
+Data Recharging, exploits user interest and preference information to deliver data 
+updates and other relevant items to users on their portable devices. The second 
+project, called Telegraph, is building an adaptive data-flow processing architecture to 
+process long-running queries over variable streams of data, as would arise in sensor- 
+based and other highly dynamic data environments. 
+
+Challenges in Ubiquitous Data Management 
+
+Data Recharging: Profile-Based Data Dissemination and Synchronization 
+
+Mobile devices require two key resources to function: power and data. The mobile 
+nature of such devices combined with limitations of size and cost makes it impractical 
+to keep them continually connected to the fixed power and data (i.e., the Internet) 
+grids. Mobile devices cope with disconnection from these grids by "caching". 
+Devices use rechargeable batteries for caching power, while local storage is used for 
+caching data. Periodically, the device-local local resources must be "recharged" by 
+connecting with the fixed power and data grids. With existing technology, however, 
+the process of recharging the device resident data is much more cumbersome and 
+error-prone than recharging the power. Power recharging can be done virtually 
+anywhere in the world, requires little user involvement, and works progressively - 
+the longer the device is recharged, the better the device-stored power becomes. In 
+contrast, data "recharging" has none of these attributes. 
+
+The Data Recharging project is developing a service and corresponding 
+infrastructure that permits a mobile device of any kind to plug into the Internet at any 
+location for any amount of time and as a result, end up with more useful data than it 
+had before [CFZOO]. As with power recharging, the initiation of a data charge simply 
+requires "plugging in" a device to the network. The longer the device is left plugged 
+in, the more effective the charge. Although similar to battery recharging, data 
+recharging is more complex; the type and amount of data delivered during a data 
+charge must be tailored to the needs of the user, the capabilities of the recharged 
+device, and the tasks that the recharged data is needed to support. 
+
+Different mobile users will have different data needs. A business traveler may 
+want updates of contact information, restaurant reviews and hotel pricing guides 
+specific to a travel destination. Students may require access to recent course notes, 
+required readings for the next lecture and notification about lab space as it becomes 
+available. Data recharging represents specifications of user needs as profiles. 
+Profiles can be thought of as long-running queries that continually sift through the 
+available data to find relevant items and determine their value to the user. 
+
+Profiles for data recharging contain three types of information: First, the profile 
+describes the types of data that are of interest to the user. This description must be 
+declarative in nature, so that it can encompass newly created data in addition to 
+existing data. The description must also be flexible enough to express predicates 
+over different types of data and media. Second, because of limitations on bandwidth, 
+device-local storage, and recharging time, only a bounded amount of information can 
+be sent to a device during data recharging. Thus, the profile must also express the 
+user s preferences in terms of priorities among data items, desired resolutions of 
+multi-resolution items, consistency requirements, and other properties. Finally, user 
+context can be dynamically incorporated into the recharging process by 
+parameterizing the user profile with information obtained from the device-resident 
+Personal Information Management (PIM) applications such as the calendar, contact 
+list, and To Do list. 
+
+Our previous work on user profiles has focused on 1) efficiently processing 
+profiles over streams of XML documents (i.e., the XFilter system) [AFOO], 2) 
+learning and maintaining user profiles based on explicit user feedback [CFGOO], and 
+3) development of a large-scale, reliable system for mobile device synchronization 
+[DFOO]. Data recharging can build upon this work, but requires the further 
+development of a suitable language and processing strategy for highly expressive user 
+profiles (i.e., that include user preference and contextual information), and the 
+development of a scalable, wide-area architecture that is capable of providing a data 
+recharging service on a global basis to potentially millions of users. 
+
+Adaptive Dataflow Query Processing 
+
+A second important aspect of ubiquitous computing environments is the variable 
+nature of data availability and the challenges of managing and processing dynamic 
+data flows. In mobile applications for example, data can move throughout the system 
+in order to follow the users who need it. Conversely, in mobile applications where 
+the data is being created at the endpoints (say, for example, a remote sensing 
+application) data streams into the system in an erratic fashion to be processed, stored, 
+and possibly acted upon by agents residing in the network. Information flows also 
+arise in other applications, such as data dissemination systems in whieh streams of 
+newly created and modified data must be routed to users and shared caches. 
+
+Traditional database query processing systems break down in such environments 
+for a number of reasons: First, they are based on static approaches to query 
+
+optimization and planning. Database systems produce query plans using simple cost 
+models and statistics about the data to estimate the cost of running particular plans. 
+In a dynamic dataflow environment, this approach simply does not work because 
+there are typically no reliable statistics about the data and because the arrival rates, 
+order, and behavior of the data streams are too unpredictable [UFA98]. 
+
+Second, the exisiting approaches cannot adequately cope with failures that arise 
+during the processing of a query. In current database systems, if the failure of a data 
+source goes undetected, the query processor simply blocks, waiting for the data to 
+arrive. If a failure is detected, then a query is simply aborted and restarted. Neither 
+of these situations is appropriate in a ubiquitous computing environment in which 
+sources and streams behave unpredictably, and queries can be extremely long-running 
+(perhaps even continuous ). 
+
+Third, existing approaches are optimized for a batch style of processing in which 
+the goal is to deliver an entire answer (i.e., they are optimized for the delivery of the 
+last result). In a ubiquitous computing environment, where users will be interacting 
+with the system in a fine-grained fashion, such approaches are unacceptable. 
+Processed data (e.g., query results, event notifications, etc.) must be passed on to the 
+user as soon as they are available. Furthermore, because the system is interactive, a 
+user may choose to modify the query on the basis of previously returned information 
+or other factors. Thus, the system must be able to gracefully adjust to changes in the 
+needs of the users [HACO-i-99]. 
+
+The Telegraph project at UC Berkeley [HFCD-l-00] is investigating these issues 
+through the development of an adaptive dataflow processing engine. Telegraph uses a 
+novel approach to query execution based on eddies , which are dataflow control 
+structures that route data to query operators on an item-by-item basis [AHOO]. 
+Telegraph does not rely upon a traditional query plan, but rather, allows the plan to 
+develop and adapt during the execution. For queries over continuous streams of data. 
+the system can continually adapt to changes in the data arrival rates, data 
+characteristics, and the availability of processing, storage, and communication 
+resources. 
+
+In addition to novel control structures. Telegraph also employs non-blocking, 
+symmetric query processing operators, such as XJoins [UFOO] and Ripple Joins 
+[HH99], which can cope with changing and unpredictable arrival of their input data. 
+The challenges being addressed in the Telegraph project include the development of 
+cluster-based and wide-area implementations of the processing engine, the design of 
+fault-tolerance mechanisms (particularly for long-running queries), support for 
+continuous queries over sensor data and for profile-based information dissemination, 
+and user interface issues. 
+
+Conclusions 
+
+Ubiquitous computing is a compelling vision for the future that is moving closer to 
+realization at an accelerating pace. The combination of global wireless and wired 
+connectivity along with increasingly small and powerful devices enables a wide array 
+of new applications that will change the nature of computing. Beyond new devices 
+and communications mechanisms, however, the key technology that is required to 
+make ubiquitous computing a reality is data management. Data lies at the heart of all 
+ubiquitous computing applications, but these applications and environments impose 
+new and challenging requirements for data management technology. 
+
+In this short paper, I have tried to outline the key aspects of ubiquitous computing 
+from a data management perspective. These aspects were organized into three 
+categories: 1) support for mobility, 2) context-awareness, and 3) support for 
+collaboration. I then examined each of these to determine a set of requirements that 
+they impose on data management. The over-riding issue that stems from all of these 
+is the need for adaptivity. Thus, traditional data management techniques, which tend 
+to be static and fairly rigid, must be rethought in light of this emerging computing 
+environment. 
+
+I also described two on-going projects that are re-examining key aspects of data 
+management techniques: the DataRecharging project, which aims to provide data 
+synchronization and dissemination of highly relevant data for mobile users based on 
+the processing of sophisticated user profiles; and the Telegraph project, which is 
+developing a dynamic dataflow processing engine to efficiently and adaptively 
+process streams of data from a host of sources ranging from web sources to networks 
+of sensors. 
+
+Of course, there are a number of very important data management issues that have 
+not been touched upon in this short paper. First of all, the ability to interoperate 
+among multiple applications and types of data will depend on standards for data- 
+interchange, resource discovery, and inter-object communication. Great strides are 
+being made in these areas, and the research issues are only a small part of the effort 
+involved in the standardization process. Another important area, in which on-going 
+research plays a central role, is the development of global-scale, secure and archival 
+information storage utilities. An example of such a system is the OceanStore utility, 
+currently under development at UC Berkeley [KBCC+00]. 
+
+
+