SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Materialized views: techniques, implementations, and applications
Materialized views: techniques, implementations, and applications
Optimization of sequence queries in database systems
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Fast Computation of Sparse Datacubes
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Computing Iceberg Queries Efficiently
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The Design and Implementation of a Sequence Database System
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Summarizability in OLAP and Statistical Data Bases
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
SRQL: Sorted Relational Query Language
SSDBM '98 Proceedings of the 10th International Conference on Scientific and Statistical Database Management
RFID Handbook: Fundamentals and Applications in Contactless Smart Cards and Identification
RFID Handbook: Fundamentals and Applications in Contactless Smart Cards and Identification
Warehousing and Analyzing Massive RFID Data Sets
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Flowcube: constructing RFID flowcubes for multi-dimensional analysis of commodity flows
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Why you should run TPC-DS: a workload analysis
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Visual cube and on-line analytical processing of images
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
| Hi-index | 0.00 |
We design a novel online analytical processing system for sequence data analysis (the S-OLAP system). The biggest distinction of S-OLAP from traditional OLAP is that a sequence can be characterized not only by the attributes' values of its constituting items, but also by the subsequence/substring patterns it possesses. Traditional OLAP systems and techniques were not designed for sequence data and thus they are incapable of supporting sequence data analysis. This paper describes my Ph.D research on the design and implementation of the S-OLAP system. The proposed system is able to support "pattern-based" grouping and aggregation, which is currently not supported by any OLAP system. The preliminary ideas, significant research issues as well as major challenges of this ongoing project are presented.