Processing count queries over event streams at multiple time granularities

Abstract

Management and analysis of streaming data has become crucial with its applications to web, sensor data, network traffic data, and stock market. Data streams consist of mostly numeric data but what is more interesting are the events derived from the numerical data that need to be monitored. The events obtained from streaming data form event streams. Event streams have similar properties to data streams, i.e., they are seen only once in a fixed order as a continuous stream. Events appearing in the event stream have time stamps associated with them at a certain time granularity, such as second, minute, or hour. One type of frequently asked queries over event streams are count queries, i.e., the frequency of an event occurrence over time. Count queries can be answered over event streams easily, however, users may ask queries over different time granularities as well. For example, a broker may ask how many times a stock increased in the same time frame, where the time frames specified could be an hour, day, or both. Such types of queries are challenging especially in the case of event streams where only a window of an event stream is available at a certain time instead of the whole stream. In this paper, we propose a technique for predicting the frequencies of event occurrences in event streams at

Efficient algorithms for coding Hilbert curve of arbitrary-sized image and application to window query

“…Instead of using the block-based scanning order, it seems that our proposed snake scan-based algorithm for coding the Hilbert curve of an arbitrary-sized image can be applied to Wang et al's motion estimation algorithm in order to have better estimation accuracy and to generalize their result to the arbitrary-sized image domain. In addition, it is also an interesting research issue to extend the results of this paper to the other applications such as retrieval [38], region segmentation [12], moment computation [13], neighbor-finding, watermarking, alternative patterns, cost modeling of spatial operators [20], count queries [41], image coding, and so on.…”

Finding event correlations in federated wireless sensor networks

“…CQL syntax and semantics are quite similar to that of Structured Query Language (SQL), but there are additional clauses such as WINDOW and EVERY that support sliding or tumbling window-based analysis over streams. CQL has basic stream filtering (SELECT x,y FROM Stream WHERE) queries as well as clauses for algebraic (COUNT [22], SUM, AVERAGE) and holistic (MIN, MAX) aggregation. More complex aggregation functions such as TOP-K [16], DISTINCT, QUANTILES, and SKYLINE can also be included in the CQL.…”

A multi-dimensional indexing approach for timestamped event sequence matching

“…Let us compute the distance between E 4 and E 5 within the time window whose data rectangle is ( [20,20], [20,20], [9,9], [3,10], [18,18]). The range of the 4th dimension is [3,10]; therefore, its size is 10 À 3 ¼ 7.…”

“…The range of the 5th dimension is [18,18]; therefore, its size is 18 À 8 = 0. When we merge these two dimensions, we have the range of ½MINð3; 18Þ; MAXð10; 18Þ ¼ ½3; 18 for the merged dimension.…”

“…During the past decades, there have been many research efforts on similar sequence matching in sequence databases. Based on these results, many useful techniques for similar sequence matching have been proposed [1,2,5,7,9,11,[14][15][16]18,[21][22][23].…”