Continuous Ouery (CO) systems typically exploit commonality among query expressions to achieve improved efficiency through shared processing. Recently proposed CO systems have introduced window specifications in order to support unbounded data streams. There has been, however, little investigation of sharing for windowed query operators.

The widespread use of sensor networks presents revolutionary opportunities for life and environmental science applications. Many of these applications involve continuous queries that require the tracking, monitoring, and correlation of multi-sensor data that represent moving objects. We propose to answer these queries using a multi-way stream window join operator. This form of join over multisensor data must cope with the infinite nature of sensor data streams and the delays in network transmission. This paper introduces a class of join algorithms, termed W-join, for joining multiple infinite data streams. W-join addresses the infinite nature of the data streams by joining stream data items that lie within a sliding window and that match a certain join condition. W-join can be used to track the motion of a moving object or detect the propagation of clouds of hazardous material or pollution spills over time in a sensor network environment. We describe two new algorithms for W-join, and address variations and local/global optimizations related to specifying the nature of the window constraints to fulfill the posed queries. The performance of the proposed algorithms are studied experimentally in a prototype stream database system, using synthetic data streams and real time-series data. Tradeoffs of the proposed algorithms and their advantages and disadvantages are highlighted, given variations in the aggregate arrival rates of the input data streams and the desired response times per query.

This demonstration presents the design of "STEAM", Purdue Boiler Makers ' stream database system that allows for the processing of continuous and snap-shot queries over data streams. Specifically, the demonstration will

The need to search for complex and recurring patterns in database sequences is shared by many applications. In this work, we discuss how to express and support efficiently sophisticated sequential pattern queries in relational database systems. Thus, we first

The emergence of location-aware services calls for new real-time spatio-temporal query processing algorithms that deal with large numbers of mobile objects and queries. In this demo, we present PLACE (Pervasive Location-Aware Computing Environments); a scalable location-aware database server developed at Purdue University. The PLACE server addresses scalability by adopting an incremental evaluation mechanism for answering concurrently executing continuous spatiotemporal queries. The PLACE server supports a wide variety of stationery and moving continuous spatio-temporal queries through a set of pipelined spatio-temporal operators. The large numbers of moving objects generate real-time spatio-temporal data streams.

this paper, we investigate the design and optimization of a query language capable of expressing and supporting efficiently the search for complex sequential patterns in database systems. Thus, we first introduce SQL-TS, an extension of SQL to express these patterns, and then we study how to optimize the queries for this language. We take the optimal text search algorithm of Knuth, Morris and Pratt, and generalize it to handle complex queries on sequences. Our algorithm exploits the interdependencies between the elements of a pattern to minimize repeated passes over the same data. Experimental results on typical sequence queries, such as double bottom queries, confirm that substantial speedups are achieved by our new optimization techniques

The tremendous increase of cellular phones, GPS-like devices, and RFIDs results in highly dynamic environments where objects as well as queries are continuously moving. In this paper, we present a continuous query processor designed specifically for highly dynamic environments (e.g., location-aware environments). We implemented the proposed continuous query processor inside the PLACE server (Pervasive Location-Aware Computing Environments); a scalable location-aware database server currently developed at Purdue University. The PLACE server extends data streaming management systems to support location-aware environments. Such environments are characterized by the wide variety of continuous spatio-temporal queries and the unbounded spatio-temporal streams. The proposed continuous query processor mainly includes: (1) Developing new incremental spatio-temporal operators to support a wide variety of continuous spatio-temporal queries, (2) Extending the semantic of sliding window queries to deal with spatial sliding windows as well as temporal sliding windows, and (3) Providing a shared execution framework for scalable execution of a set of concurrent continuous spatio-temporal queries. Preliminary experimental evaluation shows the promising performance of the continuous query processor of the PLACE server.

Suppose a long-running analytical query is executing on a database server and has been allocated a large amount of physical memory. A high-priority task comes in and we need to run it immediately with all available resources. We have several choices. We could swap out the old query to disk, but writing out a large execution state may take too much time. Another option is to terminate the old query and restart it after the new task completes, but we would waste all the work already performed by the old query. Yet another alternative is to periodically checkpoint the query during execution, but traditional synchronous checkpointing carries high overhead. In this paper, we advocate a database-centric approach to implementing query suspension and resumption, with negligible execution overhead, bounded suspension cost, and efficient resumption. The basic idea is to let each physical query operator perform lightweight checkpointing according to its own semantics, and coordinate asynchronous checkpoints among operators through a novel contracting mechanism. At the time of suspension, we find an optimized suspend plan for the query, which may involve a combination of dumping current state to disk and going back to previous checkpoints. The plan seeks to minimize the suspend/resume overhead while observing the constraint on suspension time. Our approach requires only small changes to the iterator interface, which we have implemented in the PREDATOR database system. Experiments with our implementation demonstrate significant advantages of our approach over traditional alternatives.

Emerging data stream processing systems rely on windowing to enable on-the-fly processing of continuous queries over unbounded streams. As a result, several recent e#orts have developed window-aware implementations of query operators such as joins and aggregates. This focus on individual operators, however, ignores the larger issue of how to coordinate the pipelined execution of such operators when combined into a full windowed query plan. In this paper, we first show how the straightforward application of traditional pipelined query processing techniques to sliding window queries can result in ine#cient and incorrect behavior. We then present three alternative execution techniques that guarantee correct behavior for pipelined sliding window queries and develop new algorithms for correctly evaluating window-based duplicateelimination, Group-By and Set operators in this context. We implemented all of these techniques in a prototype data stream system and report the results of a detailed performance study of the system.

Real-world video-based applications require database technology that is capable of storing digital video in the form of video databases and providing content-based video search and retrieval. Methods for handling traditional data storage, query, search, retrieval, and presentation cannot be extended to provide this functionality. The VDBMS research initiative is motivated by the requirements of videobased applications to search and retrieve portions of video data based on content and by the need for testbed facilities to facilitate research in the area of video database management. In this paper we describe the VDBMS video database research platform, a system that supports comprehensive and efficient database management for digital video. Our fundamental concept is to provide a full range of functionality for video as a well-defined abstract database data type, with its own description, parameters, and applicable methods. Research problems that are addressed by VDBMS to support the handling of video data include MPEG7 standard multimedia content representation, algorithms for image-based shot detection, image processing techniques for extracting lowlevel visual features, a high-dimensional indexing technique to access the high-dimensional feature vectors extracted by image preprocessing, multimedia query processing and optimization, new query operators, real-time stream management, a search-based buffer management policy, and an access control model for selective, content-based access to streaming video. VDBMS also provides an environment for testing the correctness and scope of new video processing techniques, measuring the performance of algorithms in a standardized way, and comparing the performance of different implementations of an algorithm or component. We are ...