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In this paper, we present an efficient B +-tree based indexing method, called iDistance, for K-nearest neighbor (KNN) search in a high-dimensional metric space. iDistance partitions the data based on a space-or data-partitioning strategy, and selects a reference point for each partition. The data points in each partition are transformed into a single(More)
The moving k nearest neighbor (MkNN) query finds the k nearest neighbors of a moving query point continuously. The high potential of reducing the query processing cost as well as the large spectrum of associated applications have attracted considerable attention to this query type from the database community. This paper presents an incremental(More)
In data stream applications, data arrive continuously and can only be scanned once as the query processor has very limited memory (relative to the size of the stream) to work with. Hence, queries on data streams do not have access to the entire data set and query answers are typically approximate. While there have been many studies on the k Nearest(More)
This paper assumes a setting where a population of objects move continuously in the Euclidean plane. The position of each object, modeled as a linear function from time to points, is assumed known. In this setting, the paper studies the querying for dense regions. In particular, the paper defines a particular type of density query with desirable properties(More)
—Destination prediction is an essential task for many emerging location based applications such as recommending sightseeing places and targeted advertising based on destination. A common approach to destination prediction is to derive the probability of a location being the destination based on historical trajectories. However, existing techniques using(More)
Monitoring aggregates on IP traffic data streams is a compelling application for data stream management systems. The need for exploratory IP traffic data analysis naturally leads to posing related aggregation queries on data streams, that differ only in the choice of grouping attributes. In this paper, we address this problem of efficiently computing(More)
The effectiveness of many existing high-dimensional indexing structures is limited to specific types of queries and workloads. For example, while the Pyramid technique and the iMinMax are efficient for window queries, the iDistance is superior for kNN queries. In this paper, we present a new structure, called the P +-tree, that supports both window queries(More)
— Detecting changes in stock prices is a well known problem in finance with important implications for monitoring and business intelligence. Forewarning of changes in stock price, can be made by the early detection of changes in the distributions of stock order numbers. In this paper, we address the change detection problem for streams of stock order(More)
Multidimensional data points can be mapped to one-dimensional space to exploit single dimensional indexing structures such as the B<sup>&plus;</sup>-tree. In this article we present a Generalized structure for data Mapping and query Processing (GiMP), which supports extensible mapping methods and query processing. GiMP can be easily customized to behave(More)