Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Balancing histogram optimality and practicality for query result size estimation
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Random sampling for histogram construction: how much is enough?
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
New algorithms for efficient mining of association rules
Information Sciences: an International Journal
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient computation of temporal aggregates with range predicates
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Processing complex aggregate queries over data streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Querying and mining data streams: you only get one look a tutorial
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Accurate estimation of the number of tuples satisfying a condition
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences
IEEE Transactions on Knowledge and Data Engineering
Exploiting Data Mining Techniques for Broadcasting Data in Mobile Computing Environments
IEEE Transactions on Knowledge and Data Engineering
Temporal and spatio-temporal aggregations over data streams using multiple time granularities
Information Systems - Special issue: Best papers from EDBT 2002
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Incremental Computation and Maintenance of Temporal Aggregates
Proceedings of the 17th International Conference on Data Engineering
CRB-Tree: An Efficient Indexing Scheme for Range-Aggregate Queries
ICDT '03 Proceedings of the 9th International Conference on Database Theory
Sampling-Based Estimation of the Number of Distinct Values of an Attribute
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Fast Incremental Maintenance of Approximate Histograms
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Scalable Algorithms for Large Temporal Aggregation
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Approximate Query Answering with Frequent Sets and Maximum Entropy
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
The optimization of queries in relational databases
The optimization of queries in relational databases
Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data
IEEE Transactions on Knowledge and Data Engineering
Reliable Detection of Episodes in Event Sequences
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Dependency detection in MobiMine: a systems perspective
Information Sciences—Informatics and Computer Science: An International Journal - special issue: Knowledge discovery from distributed information sources
Approximate Temporal Aggregation
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Main Memory-Based Algorithms for Efficient Parallel Aggregation for Temporal Databases
Distributed and Parallel Databases
Detection of Significant Sets of Episodes in Event Sequences
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Spatiotemporal Aggregate Computation: A Survey
IEEE Transactions on Knowledge and Data Engineering
An efficient method for temporal aggregation with range-condition attributes
Information Sciences—Informatics and Computer Science: An International Journal
Dynamic histograms for future spatiotemporal range predicates
Information Sciences: an International Journal
Looking into the seeds of time: Discovering temporal patterns in large transaction sets
Information Sciences: an International Journal
Information Sciences: an International Journal
Aggregation of infinite sequences
Information Sciences: an International Journal
Effective Similarity Analysis over Event Streams Based on Sharing Extent
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Hi-index | 0.07 |
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 multiple time granularities. The proposed approximation method efficiently estimates the count of events with a high accuracy in an event stream at any time granularity by examining the distance distributions of event occurrences. The proposed method has been implemented and tested on different real data sets including daily price changes in two different stock exchange markets. The obtained results show its effectiveness.