Consider a database that represents information about moving objects and their location. For example, for a database representing the location of taxi-cabs a typical query may be: retrieve the free cabs that are currently within 1 mile of 33 N. Michigan Ave., Chicago (to pick-up a customer). In the military, moving objects database applications arise in the context of the digital battlefield, and in the civilian industry they arise in transportation systems. Currently, moving objects database applications are being developed in an ad hoc fashion. Database Management System (DBMS) technology provides a potential foundation upon which to develop these applications, however, DBMS's are currently not used for this purpose. The reason is that there is a critical set of capabilities that are needed by moving objects database applications and are lacking in existing DBMS's. The objective of our Databases fOr MovINg Objects (DOMINO) project is to build an envelope containing these capabilities...

In this paper we consider databases representing information about moving objects (e.g. vehicles), particularly their location. We address the problems of updating and querying such databases. Specifically, the update problem is to determine when the location of a moving object in the database (namely its database location) should be updated. We answer this question by proposing an information cost model that captures uncertainty, deviation, and communication. Then we analyze dead-reckoning policies, namely policies that update the database location whenever the distance between the actual location and the database location exceeds a given threshold, x. Deadreckoning is the prevalent approach in military applications, and our cost model enables us to determine the threshold x. We propose several dead-reckoning policies and we compare their performance by simulation. Then we consider the problem of processing range queries in the database. An example of a range query is `retrieve the ob...

In current distributed systems, the notion of mobility is emerging in many forms and applications.

We consider the problem of managing the information required to locate users in a wireless communication system, with a focus on designing and evaluating location management techniques that are efficient, scalable, and flexible. The three key contributions of this paper are: (1) A family of location management techniques, HiPER (for Hierarchical ProfilE Replication), that efficiently provide life-long (non-geographic) numbering with fast location lookup; (2) Pleiades, a scalable event-driven wireless system simulator with realistic calling and mobility patterns derived from several months of real traffic traces; and (3) multi-day simulations comparing our proposed location management techniques with current and previously proposed techniques on a realistic geographical and network topology. Research supported by the Center for Telecommunications and the Center for Integrated Systems at Stanford University, and by equipment grants from Digital and IBM Corporations. 1 Introduction I...

Transparent data replication has been considered a promising technique for improving system performance for a large distributed network. In this paper, a hybrid transparent replication model is presented. We address the problems of replication proxy placement in the network and data replica placement on the installed proxies given that a maximum of proxies are allowed. Both reads and writes are considered in these problems. The performance objective is to minimize the total data transfer cost. To address the placement problems, we first present the optimal solutions for a single object in a tree network without/with constraint on the number of replicas. Based on that, two schemes, namely, AGGregate Access (AGGA) and Weighted POPularity (WPOP),are proposed for the replication proxy placement problem. An optimal solution is described for the replica placement problem. The performance of the proposed placement schemes is evaluated with a set of carefully designed simulation experiments over a wide range of system parameters. The results give us several helpful intuitions in deploying transparent replication proxies in a practical system.

In this paper we consider databases representing information about moving objects (e.g. vehicles), particularly their location. We address the problems of updating and querying such databases. Specifically, the update problem is to determine when the location of a moving object in the database (namely its database location) should be updated. We answer this question by proposing an information cost model that captures uncertainty, deviation, and communication. Then we analyze dead-reckoning policies, namely policies that update the database location whenever the distance between the actual location and the database location exceeds a given threshold, x. Dead-reckoning is the prevalent approach in military applications, and our cost model enables us to determine the threshold x. Then we consider the problem of processing range queries in the database, and we propose a probabilistic algorithm to solve the problem.

Abstract—In this paper, we present a new data mining algorithm which involves incremental mining for user moving patterns in a mobile computing environment and exploit the mining results to develop data allocation schemes so as to improve the overall performance of a mobile system. First, we propose an algorithm to capture the frequent user moving patterns from a set of log data in a mobile environment. The algorithm proposed is enhanced with the incremental mining capability and is able to discover new moving patterns efficiently without compromising the quality of results obtained. Then, in light of mining results of user moving patterns and the properties of data objects, we develop data allocation schemes that can utilize the knowledge of user moving patterns for proper allocation of both personal and shared data. By employing the data allocation schemes, the occurrences of costly remote accesses can be minimized and the performance of a mobile computing system is thus improved. For personal data allocation, two data allocation schemes, which explore different levels of mining results, are devised: one utilizes the set level of moving patterns and the other utilizes the path level of moving patterns. As can be seen later, the former is useful for the allocation of read-intensive data objects, whereas the latter is good for the allocation of update-intensive data objects. The data allocation schemes for shared data, which are able to achieve local optimization and global optimization, are also developed. Performance of these data allocation schemes is comparatively analyzed. It is shown by our simulation results that the knowledge obtained from the user moving patterns is very important in devising effective data allocation schemes which can lead to significant performance improvement in a mobile computing system. Index Terms—Data mining, mobile computing, user moving patterns, data allocation scheme, mobile database. 1

The problem of allocating discrete computational resources motivates interest in general multi-unit combinatorial exchanges. This paper considers the problem of computing optimal (surplus-maximizing) allocations, assuming unrestricted quasi-linear preferences. We present a solver whose pseudo-polynomial time and memory requirements are linear in three of four natural measures of problem size: number of agents, length of bids, and units of each resource. In applications where the number of resource types is inherently a small constant, e.g., computational resource allocation, such a solver offers advantages over more elaborate approaches developed for high-dimensional problems.

In this paper we first introduce Moving Objects Databases and their related research problems. Then we concentrate on a particular problem, namely reducing the information cost associated with a trip taken by some moving object (e.g. a vehicle). The information cost of a trip consists of the overhead of position-update messages and the deviation of the database position from the actual position of the object. We introduce two position update policies, namely plain dead reckoning (pdr) and adaptive dead reckoning (adr). We show that adr has a lower information cost than pdr. 2 Introduction 2.1 Moving Objects Databases and Relevant Research Consider a database that represents information about moving objects and their position. For example, for a database representing the location of taxi-cabs a typical query may be: retrieve the free cabs that are currently within 1 mile of 33 N. Michigan Ave., Chicago (to pick-up a customer); or for a trucking company database a typical query may be:...

Multimedia data is difficult to query since much of what user would like to query is contained with the semantic meaning of the object. Extracting semantics by analyzing the multimedia object through computational means is difficult, and contentbased retrieval multimedia systems are still a matter of research. In this paper we discuss "context-based" indexing, which is another approach to determine the semantic meaning of multimedia objects. Context information includes geographical locations, identities and activities that we can garner from sensors in the environment. We propose extensions to the MIRA architecture, a multimedia repository we have implemented for context-based recordings of Internet videoconferences to support more generic context services for location and identities. We discuss a framework for designing services based on Bayesian Networks for probabilistic analysis of sensor information Keywords Context-based computing, multimedia databases, indexing, sensor fusion...