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About 10,000 papers for “data streams”
Gibbins, Nicholas · EdShare Southampton (University of Southampton)
Traditional DBMS makes several assumptions: persistent data storage relatively static records (typically) no predefined notion of time complex one-off queries Big Data -The Four Vs 5
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2007
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HweeHwa Pang, Kian‐Lee Tan · Synthesis lectures on data management · 2012
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Gibbins, Nicholas · EdShare Southampton (University of Southampton) · 2013
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For The Ecml/pkdd, Mohamed Gaber, Joao Gama et al. · 2007
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David Both · Apress eBooks · 2019
In this chapter you will learn
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Gibbins, Nicholas · EdShare Southampton (University of Southampton) · 2015
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P. Tidwell · 2005
The full throughput of a Virtex™-4 DSP48 slice can be achieved by time-multiplexing two data streams with a double data rate (DDR) technique. Alpha blending is an example of this technique. This application note describes a reference design file (xapp706.zip) available on the Xilinx web site.
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Nicholas Generous (505428), Kristen J. Margevicius (505427), Kirsten J. Taylor-McCabe (505429) et al. · Figshare · 2015
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David Both · Apress eBooks · 2018
Everything in Linux revolves around streams of data – particularly text streams.
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Fatma Mohamed, Rasha M. Ismail, Nagwa Badr et al. · Advances in computational intelligence and robotics book series · 2017
Many modern applications in several domains such as sensor networks, financial applications, web logs and click-streams operate on continuous, unbounded, rapid, time-varying streams of data elements. These applications present new challenges that are not addressed by traditional data management techniques. For the query processing of continuous data streams, we consider in particular continuous queries which are evaluated continuously as data streams continue to arrive. The answer to a continuous query is produced over time, always reflecting the stream data seen so far. One of the most critical requirements of stream processing is fast processing. So, parallel and distributed processing would be good solutions. This paper gives (1) analysis to the different continuous query processing techniques; (2) a comparative study for the data streams execution environments; and (3) finally, we propose an integrated system for processing data streams based on cloud computing which apply continuous query optimization technique on cloud environment.
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Elliot Cohen · 2024
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S. Sasikala, Devi D Renuka, Raghvendra Kumar · Apple Academic Press eBooks · 2023
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Tamraparni Dasu, Gary M. Weiss · IGI Global eBooks · 2011
When a space shuttle takes off, tiny sensors measure thousands of data points every fraction of a second, pertaining to a variety of attributes like temperature, acceleration, pressure and velocity. A data gathering server at a networking company receives terabytes of data a day from various network elements like routers, reporting on traffic throughput, CPU usage, machine loads and performance. Each of these is an example of a data stream. Many applications of data streams arise naturally in industry (networking, e-commerce) and scientific fields (meteorology, rocketry). Data streams pose three unique challenges that make them interesting from a data mining perspective. 1. Size: The number of measurements as well as the number of attributes (variables) is very large. For instance, an IP network has thousands of elements each of which collects data every few seconds on multiple attributes like traffic, load, resource availability, topography, configuration and connections. 2. Rate of accumulation: The data arrives very rapidly, like “water from a fire hydrant”. Data storage and analysis techniques need to keep up with the data to avoid insurmountable backlogs. 3. Data transience: We get to see the raw data points at most once since the volumes of the raw data are too high to store or access. Request access from your librarian to read this chapter's full text.
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Hanady M. Abdulsalam, David B. Skillicorn, Pat Martin · IGI Global eBooks · 2011
Data analysis or data mining have been applied to data produced by many kinds of systems. Some systems, for example road traffic monitoring, produce data continuously, and often at high rates. Analyzing such data creates new issues, because it is neither appropriate, nor perhaps possible, to accumulate it and process it using standard data-mining techniques. The information implicit in each data record must be extracted in a limited amount of time and, usually, without the possibility of going back to consider it again. Existing algorithms must be modified to apply in this new setting. This chapter outlines and analyzes the most recent research work in the area of data-stream mining. It gives some sample research ideas or algorithms in this field and concludes with a comparison that shows the main advantages and disadvantages of the algorithms. It also includes a discussion and possible future work in the area.Request access from your librarian to read this chapter's full text.
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Andrew McGregor · Scholarly Commons (University of Pennsylvania) · 2007
Data streams are ubiquitous. Examples include the network traffic flowing past a router, data generated by an SQL query, readings taken in a sensor network, and files read from an external memory device. The data-stream model abstracts the main algorithmic constraints when processing such data: sequential access to data, limited time to process each data item, and limited working memory. The challenge of designing efficient algorithms in this model has enjoyed significant attention over the last ten years. In this thesis we investigate two new directions of data-streams research: stochastic streams where data-streams are considered through a statistical or learning-theoretic lens and graph streams where highly-structured data needs to be processed. Much of the work in this thesis is motivated by the following general questions. Stream Ordering: Almost all prior work has considered streams that are assumed to be adversarially ordered. What happens if we relax this assumption? Multiple Passes : Previous work has focused on the single-pass model. What trade-offs arise if algorithms are permitted multiple passes over the stream. Space-Efficient Sampling: Rather than approximating functions of the empirical data in the stream, what can be learned about the source of the stream? Sketchability: A fundamental problem in the data-stream model is to compare two streams. What notions of difference can be estimated in small space? In the process of considering these questions, we present algorithms and lower-bounds for a range of problems including estimating quantiles, various forms of entropy, information divergences, graph-diameter and girth; learning histograms and piecewise-linear probability density functions; and constructing graph-matchings.
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Minos Garofalakis · Encyclopedia of Database Systems · 2016
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Suvajyoti Guha, Nita Mishra, R. Motwani et al. · 2002
We study clustering under the data stream model of computation where: given a sequence of points, the objective is to maintain a consistently good clustering of the sequence observed so far, using a small amount of memory and time. The data stream model is relevant to new classes of applications involving massive data sets, such as Web click stream analysis and multimedia data analysis. We give constant-factor approximation algorithms for the k-median problem in the data stream model of computation in a single pass. We also show negative results implying that our algorithms cannot be improved in a certain sense.
Cited by 628
Pallavi Vijay Chavan, Kalyani Pampattiwar, Ramchandra Mangrulkar · Elsevier eBooks · 2026
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Sudipto Guha, et al. · 2000
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