Introduction to algorithms
Temporal databases: theory, design, and implementation
Temporal databases: theory, design, and implementation
Semantics of time-varying information
Information Systems
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Database management systems
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
On temporal aggregate processing based on time points
Information Processing Letters
Efficient computation of temporal aggregates with range predicates
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Introduction to Algorithms: A Creative Approach
Introduction to Algorithms: A Creative Approach
Database System Concepts
Implementation techniques for main memory database systems
SIGMOD '84 Proceedings of the 1984 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
Aggregates in the Temporal Query Language TQuel
IEEE Transactions on Knowledge and Data Engineering
Incremental Computation and Maintenance of Temporal Aggregates
Proceedings of the 17th International Conference on Data Engineering
Translating Aggregate Queries into Iterative Programs
VLDB '86 Proceedings of the 12th International Conference on Very Large Data Bases
Including Group-By in Query Optimization
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Parallel Algorithms for Computing Temporal Aggregates
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Aggregation in temporal databases
Aggregation in temporal databases
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
Artificial Intelligence in Medicine
Data & Knowledge Engineering
Efficient temporal counting with bounded error
The VLDB Journal — The International Journal on Very Large Data Bases
Sequenced spatio-temporal aggregation in road networks
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Parsimonious temporal aggregation
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Intelligent visualization and exploration of time-oriented data of multiple patients
Artificial Intelligence in Medicine
Sequenced spatiotemporal aggregation for coarse query granularities
The VLDB Journal — The International Journal on Very Large Data Bases
Multi-dimensional aggregation for temporal data
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Temporal aggregation on user-defined granularities
Journal of Intelligent Information Systems
Parsimonious temporal aggregation
The VLDB Journal — The International Journal on Very Large Data Bases
Aggregating and disaggregating flexibility objects
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
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The ability to model time-varying natures is essential to many database applications such as data warehousing and mining. However, the temporal aspects provide many unique characteristics and challenges for query processing and optimization. Among the challenges is computing temporal aggregates, which is complicated by having to compute temporal grouping. In this paper, we introduce a variety of temporal aggregation algorithms that overcome major drawbacks of previous work. First, for small-scale aggregations, both the worst-case and average-case processing time have been improved significantly. Second, for large-scale aggregations, the proposed algorithms can deal with a database that is substantially larger than the size of available memory. Third, the parallel algorithm designed on a shared-nothing architecture achieves scalable performance by delivering nearly linear scale-up and speed-up, even at the presence of data skew. The contributions made in this paper are particularly important because the rate of increase in database size and response time requirements has out-paced advancements in processor and mass storage technology.