Continuous Nearest Neighbor Queries over Sliding Windows

Abstract—This paper studies continuous monitoring of nearest neighbor (NN) queries over sliding window streams. According to this model, data points continuously stream in the system, and they are considered valid only while they belong to a sliding window that contains 1) the W most recent arrivals (count-based) or 2) the arrivals within a fixed interval W covering the most recent time stamps (time-based). The task of the query processor is to constantly maintain the result of long-running NN queries among the valid data. We present two processing techniques that apply to both count-based and time-based windows. The first one adapts conceptual partitioning, the best existing method for continuous NN monitoring over update streams, to the sliding window model. The second technique reduces the problem to skyline maintenance in the distance-time space and precomputes the future changes in the NN set. We analyze the performance of both algorithms and extend them to variations of NN search. Finally, we compare their efficiency through a comprehensive experimental evaluation. The skyline-based algorithm achieves lower CPU cost, at the expense of slightly larger space overhead. Index Terms—Location-dependent and sensitive, spatial databases, query processing, nearest neighbors, data streams, sliding windows.

Towards an Efficient, Scalable Stream Query Operator Framework for Representing and Analyzing Continuous Fields

Advancements in sensor technology have made it less expensive to deploy massive numbers of sensors to observe continuous geographic phenomena at high sample rates and stream live sensor observations. This fact has raised new challenges since sensor streams have pushed the limits of traditional geo-sensor data management technology. Data Stream Engines (DSEs) provide facilities for near real-time processing of streams, however, algorithms supporting representing and analyzing Spatio-Temporal (ST) phenomena are limited. This dissertation investigates near real-time representation and analysis of continuous ST phenomena, observed by large numbers of mobile, asynchronously sampling sensors, using a DSE and proposes two novel stream query operator frameworks. First, the ST Interpolation Stream Query Operator Framework (STI-SQO framework) continuously transforms sensor streams into rasters using a novel set of stream query operators that perform ST-IDW interpolation. A key component of the STI-SQO framework is the 3D, main memory-based, ST Grid Index that enables high performance ST insertion and deletion of massive numbers of sensor observations through Isotropic Time Cell and Time Block-based partitioning. The ST Grid Index facilitates fast ST search for samples using ST shell-based neighborhood search templates, namely the Cylindrical Shell Template and Nested Shell Template. Furthermore, the framework contains the stream-based ST-IDW algorithms ST Shell and ST ak-Shell for high performance, parallel grid cell interpolation. Secondly, the proposed ST Predicate Stream Query Operator Framework (STP-SQO framework) efficiently evaluates value predicates over ST streams of ST continuous phenomena. The framework contains several stream-based predicate evaluation algorithms, including Region-Growing, Tile-based, and Phenomenon-Aware algorithms, that target predicate evaluation to regions with seed points and minimize the number of raster cells that are interpolated when evaluating value predicates. The performance of the proposed frameworks was assessed with regard to prediction accuracy of output results and runtime. The STI-SQO framework achieved a processing throughput of 250,000 observations in 2.5 s with a Normalized Root Mean Square Error under 0.19 using a 500×500 grid. The STP-SQO framework processed over 250,000 observations in under 0.25 s for predicate results covering less than 40% of the observation area, and the Scan Line Region Growing algorithm was consistently the fastest algorithm tested

Attribute Relationship Analysis in Outlier Mining and Stream Processing

The main theme of this thesis is to unite two important fields of data analysis, outlier mining and attribute relationship analysis. In this work we establish the connection between these two fields. We present techniques which exploit this connection, allowing to improve outlier detection in high dimensional data. In the second part of the thesis we extend our work to the emerging topic of data streams