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37
Indexing the Positions of Continuously Moving Objects
, 2000
"... The coming years will witness dramatic advances in wireless communications as well as positioning technologies. As a result, tracking the changing positions of objects capable of continuous movement is becoming increasingly feasible and necessary. The present paper proposes a novel, R # tree base ..."
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Cited by 389 (20 self)
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The coming years will witness dramatic advances in wireless communications as well as positioning technologies. As a result, tracking the changing positions of objects capable of continuous movement is becoming increasingly feasible and necessary. The present paper proposes a novel, R # tree based indexing technique that supports the efficient querying of the current and projected future positions of such moving objects. The technique is capable of indexing objects moving in one, two, and threedimensional space. Update algorithms enable the index to accommodate a dynamic data set, where objects may appear and disappear, and where changes occur in the anticipated positions of existing objects. A comprehensive performance study is reported.
Geometric Range Searching and Its Relatives
 CONTEMPORARY MATHEMATICS
"... ... process a set S of points in so that the points of S lying inside a query R region can be reported or counted quickly. Wesurvey the known techniques and data structures for range searching and describe their application to other related searching problems. ..."
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Cited by 266 (39 self)
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... process a set S of points in so that the points of S lying inside a query R region can be reported or counted quickly. Wesurvey the known techniques and data structures for range searching and describe their application to other related searching problems.
On Indexing Mobile Objects
, 1999
"... We show how to index mobile objects in one and two dimensions using efficient dynamic external memory data structures. The problem is motivated by real life applications in traffic monitoring, intelligent navigation and mobile communications domains. For the 1dimensional case, we give (i) a dynamic ..."
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Cited by 217 (16 self)
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We show how to index mobile objects in one and two dimensions using efficient dynamic external memory data structures. The problem is motivated by real life applications in traffic monitoring, intelligent navigation and mobile communications domains. For the 1dimensional case, we give (i) a dynamic, external memory algorithm with guaranteed worst case performance and linear space and (ii) a practical approximation algorithm also in the dynamic, external memory setting, which has linear space and expected logarithmic query time. We also give an algorithm with guaranteed logarithmic query time for a restricted version of the problem. We present extensions of our techniques to two dimensions. In addition we give a lower bound on the number of I/O's needed to answer the ddimensional problem. Initial experimental results and comparisons to traditional indexing approaches are also included. 1 Introduction Traditional database management systems assume that data stored in the database rem...
Efficient Indexing Methods for Probabilistic Threshold Queries over Uncertain Data
 Proc. 30th Int’l Conf. Very Large Data Bases (VLDB
, 2004
"... It is infeasible for a sensor database to contain the exact value of each sensor at all points in time. This uncertainty is inherent in these systems due to measurement and sampling errors, and resource limitations. In order to avoid drawing erroneous conclusions based upon stale data, the use of un ..."
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Cited by 123 (22 self)
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It is infeasible for a sensor database to contain the exact value of each sensor at all points in time. This uncertainty is inherent in these systems due to measurement and sampling errors, and resource limitations. In order to avoid drawing erroneous conclusions based upon stale data, the use of uncertainty intervals that model each data item as a range and associated probability density function (pdf) rather than a single value has recently been proposed. Querying these uncertain data introduces imprecision into answers, in the form of probability values that specify the likeliness the answer satisfies the query. These queries are more expensive to evaluate than their traditional counterparts but are guaranteed to be correct and more informative due to the probabilities accompanying the answers. Although the answer probabilities are useful, for many applications, it is only necessary to know whether the probability exceeds a given threshold – we term these Probabilistic Threshold Queries (PTQ). In this paper we address the efficient computation of these types of queries. In particular, we develop two index structures and associated algorithms to efficiently answer PTQs. The first index scheme is based on the idea of augmenting uncertainty information to an Rtree. We establish the difficulty
Spatiotemporal Access Methods
 IEEE Data Engineering Bulletin
, 2003
"... The rapid increase in spatiotemporal applications calls for new auxiliary indexing structures. A typical spatiotemporal application is one that tracks the behavior of moving objects through locationaware devices (e.g., GPS). Through the last decade, many spatiotemporal access methods are develop ..."
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Cited by 61 (8 self)
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The rapid increase in spatiotemporal applications calls for new auxiliary indexing structures. A typical spatiotemporal application is one that tracks the behavior of moving objects through locationaware devices (e.g., GPS). Through the last decade, many spatiotemporal access methods are developed. Spatiotemporal access methods focus on two orthogonal directions: (1) Indexing the past, (2) Indexing the current and predicted future positions. In this short survey, we classify spatiotemporal access methods for each direction based on their underlying structure with a brief discussion of future research directions.
Efficient Searching with Linear Constraints (Extended Abstract)
"... We show how to preprocess a set S of points in R d to get an external memory data structure that efficiently supports linearconstraint queries. Each query is in the form of a linear constraint a \Delta x b; the data structure must report all the points of S that satisfy the query. Our goal i ..."
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Cited by 56 (16 self)
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We show how to preprocess a set S of points in R d to get an external memory data structure that efficiently supports linearconstraint queries. Each query is in the form of a linear constraint a \Delta x b; the data structure must report all the points of S that satisfy the query. Our goal is to minimize the number of disk blocks required to store the data structure and the number of disk accesses (I/Os) required to answer a query. For d = 2, we present the first nearlinear size data structures that can answer linearconstraint queries using an optimal number of I/Os. We also present a linearsize data structure that can answer queries efficiently in the worst case. We combine these two approaches to obtain tradeoffs between space and query time. Finally, we show that some of our techniques extend to higher dimensions d.
Efficient Numerical Error Bounding for Replicated Network Services
 IN INT. CONF. ON VERY LARGE DATABASES (VLDB
, 2000
"... The goal of this work is to support replicated network services that accept updates to numerical records from multiple widearea locations. Given the ..."
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Cited by 42 (5 self)
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The goal of this work is to support replicated network services that accept updates to numerical records from multiple widearea locations. Given the
GADT: A Probability Space ADT for Representing and Querying the Physical World
 In ICDE
, 2002
"... Large sensor networks are being widely deployed for measurement, detection, and monitoring applications. Many of these applications involve database systems to store and process data from the physical world. This data has inherent measurement uncertainties that are properly represented by continuous ..."
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Cited by 36 (1 self)
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Large sensor networks are being widely deployed for measurement, detection, and monitoring applications. Many of these applications involve database systems to store and process data from the physical world. This data has inherent measurement uncertainties that are properly represented by continuous probability distribution functions (pdf's). We introduce a new objectrelational data type, the Gaussian ADT GADT, that models physical data as gaussian pdf's, and we show that existing index structures can be used as fast access methods for GADT data. We also present a measuretheoretic model of probabilistic data and evaluate GADT in its light.
Answering topk queries with multidimensional selections: The ranking cube approach
 In VLDB
, 2006
"... Observed in many real applications, a topk query often consists of two components to reflect a user’s preference: a selection condition and a ranking function. A user may not only propose ad hoc ranking functions, but also use different interesting subsets of the data. In many cases, a user may wan ..."
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Cited by 23 (7 self)
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Observed in many real applications, a topk query often consists of two components to reflect a user’s preference: a selection condition and a ranking function. A user may not only propose ad hoc ranking functions, but also use different interesting subsets of the data. In many cases, a user may want to have a thorough study of the data by initiating a multidimensional analysis of the topk query results. Previous work on topk query processing mainly focuses on optimizing data access according to the ranking function only. The problem of efficient answering topk queries with multidimensional selections has not been well addressed yet. This paper proposes a new computational model, called ranking cube, for efficient answering topk queries with multidimensional selections. We define a rankaware measure for the cube, capturing our goal of responding to multidimensional ranking analysis. Based on the ranking cube, an efficient query algorithm is developed which progressively retrieves data blocks until the topk results are found. The curse of dimensionality is a wellknown challenge for the data cube and we cope with this difficulty by introducing a new technique of ranking fragments. Our experiments on Microsoft’s SQL Server 2005 show that our proposed approaches have significant improvement over the previous methods. 1.
Probabilistic Reverse Nearest Neighbor Queries on Uncertain Data
 TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
"... Uncertain data is inherent in various important applications and reverse nearest neighbor (RNN) query is an important query type for many applications. While many different types of queries have been studied on uncertain data, there is no previous work on answering RNN queries on uncertain data. In ..."
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Cited by 22 (4 self)
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Uncertain data is inherent in various important applications and reverse nearest neighbor (RNN) query is an important query type for many applications. While many different types of queries have been studied on uncertain data, there is no previous work on answering RNN queries on uncertain data. In this paper, we formalize probabilistic reverse nearest neighbor query that is to retrieve the objects from the uncertain data that have higher probability than a given threshold to be the RNN of an uncertain query object. We develop an efficient algorithm based on various novel pruning approaches that solves the probabilistic RNN queries on multidimensional uncertain data. The experimental results demonstrate that our algorithm is even more efficient than a samplingbased approximate algorithm for most of the cases and is highly scalable.