The Semantic Space-Time Models of the Streamonas Data Stream Management System
ABSTRACT Four novel models define the fundamental temporal semantics upon which the stream on as DSMS has been architectured, as also the fundamental temporal semantics for application development on the system. Extensive experimental results demonstrate the power of the theoretical models as also the stability and scalability of the system when this is tested with a load from 2, 4, 6, 8 and 10 expressways when running the Linear Road Benchmark. The extensive experimental results demonstrate the effectiveness of the theoretical semantic-space time models as the system has reached the maximum level of difficulty of the benchmark (10 expressways) with an average query latency of 0.000026 seconds, 192,307 times faster than the 5 seconds hard real-time constraint the benchmark sets.
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ABSTRACT: Data stream management systems (DSMSs) receive large overheads when queries directly access the serial non-indexed incoming stream. Our novel architecture, presented in this work, addresses this problem by indexing the incoming dataflow based on a specially designed data structure. The role of this data structure is as fundamental for our DSMS as the role of a relation in a relational DBMS. The architecture achieves reusability, query parallelism and O(1) constant time complexity access to streamed data. The system managed to run the maximum level of difficulty the linear road benchmark has (10 expressways), demonstrating excellent performance results.Computer Science and Software Engineering, 2008 International Conference on; 01/2009
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ABSTRACT: Introduction We propose to demonstrate a Data Stream Management System (DSMS) called STREAM, for STanford stREam datA Manager. The challenges in building a DSMS instead of a traditional DBMS arise from two fundamental differences: In addition to managing traditional stored data such as relations, a DSMS must handle multiple continuous, unbounded, possibly rapid and time-varying data streams. Due to the continuous nature of the data, a DSMS typically supports long-running continuous queries, which are expected to produce answers in a continuous and timely fashion. STREAM is a general-purpose DSMS that supports a declarative query language and is designed to cope with high data rates and large numbers of continuous queries. A description of our current (Fall 2002) language design, algorithms, system design, and system implementation efforts can be found in [MW 03]. Needless to say, we expect additional functionality to be in place by Spring 2003. Our proposed demonstration will
Conference Paper: Gigascope: A Stream Database for Network Applications[Show abstract] [Hide abstract]
ABSTRACT: We have developed Gigascope, a stream database for network applications including traffic analysis, intrusion detection, router configuration analysis, network research, network monitoring, and performance monitoring and debugging. Gigascope is undergoing installation at many sites within the AT&T network, including at OC48 routers, for detailed monitoring. In this paper we describe our motivation for and constraints in developing Gigascope, the Gigascope architecture and query language, and performance issues. We conclude with a discussion of stream database research problems we have found in our application.Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, San Diego, California, USA, June 9-12, 2003; 01/2003