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We present the demonstration of 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 focuses on the query processing engine, "Nile". Nile extends the query processor engine of an object-relational database management system,(More)
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(More)
Two research efforts have been conducted to realize sliding-window queries in data stream management systems, namely, query revaluation and incremental evaluation. In the query reevaluation method, two consecutive windows are processed independently of each other. On the other hand, in the incremental evaluation method, the query answer for a window is(More)
The tremendous increase in the use 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(More)
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(More)
EmcT!Jing dala stnm.m processing systems rely on windowing /0 enable em-lhe-fly processing oj continuous queries Dvcr unbounded streams. A~' a resuU, swe1ll.1 recent efforL<; have developed window-aware implemcrltations of Query operators such as joins Gnd ag,qregates. 'This focus em individual operators , however, ignores the larger issuc of how to(More)
Many data stream sources are prone to periods of spikes in volume as well as periods of delays and silence. Because peak load during a spike can be orders of magnitude higher than a typical load, fully provisioning data stream monitoring system with all needed resources is generally difficult to achieve. Furthermore, data stream sources are subject to(More)
Many applications require storing data in Wireless Sensor Networks (WSNs). For example, in environmental monitoring applications. WSN may archive sensor data for retrieval at periodic intervals. In contrast to conventional network data storage, storing data in WSNs is challenging because of the limited power, memory, and communication bandwidth of WSNs.(More)