Scheduling Issues in Partitioned Temporal Join

One of the major problems of temporal databases is to develop efficient algorithms for operations that involves the time attributes. An operation that has received much attention in recent years is the temporal join which matches records from two temporal relations whose time intervals overlap. Under a partition-based algorithm, temporal data are split into partitions. During the join process, a partition in one relation only needs to join with some, but not all, partitions of the other relation. In this paper, we address scheduling issues in such an algorithm. Depending on the orders in which partitions are read, the number of I/Os incurred varies. We propose a three-phase scheduling framework to minimize the number of I/Os incurred. From the framework, a large number of scheduling strategies can be derived. We also study several representative scheduling strategies and report our findings in this paper

Generalized Lineage-Aware Temporal Windows: Supporting Outer and Anti Joins in Temporal-Probabilistic Databases

The result of a temporal-probabilistic (TP) join with negation includes, at each time point, the probability with which a tuple of a positive relation ${\bf p}$ matches none of the tuples in a negative relation ${\bf n}$, for a given join condition $\theta$. TP outer and anti joins thus resemble the characteristics of relational outer and anti joins also in the case when there exist time points at which input tuples from ${\bf p}$ have non-zero probabilities to be $true$ and input tuples from ${\bf n}$ have non-zero probabilities to be $false$, respectively. For the computation of TP joins with negation, we introduce generalized lineage-aware temporal windows, a mechanism that binds an output interval to the lineages of all the matching valid tuples of each input relation. We group the windows of two TP relations into three disjoint sets based on the way attributes, lineage expressions and intervals are produced. We compute all windows in an incremental manner, and we show that pipelined computations allow for the direct integration of our approach into PostgreSQL. We thereby alleviate the prevalent redundancies in the interval computations of existing approaches, which is proven by an extensive experimental evaluation with real-world datasets

Snapshot Semantics for Temporal Multiset Relations (Extended Version)

Snapshot semantics is widely used for evaluating queries over temporal data: temporal relations are seen as sequences of snapshot relations, and queries are evaluated at each snapshot. In this work, we demonstrate that current approaches for snapshot semantics over interval-timestamped multiset relations are subject to two bugs regarding snapshot aggregation and bag difference. We introduce a novel temporal data model based on K-relations that overcomes these bugs and prove it to correctly encode snapshot semantics. Furthermore, we present an efficient implementation of our model as a database middleware and demonstrate experimentally that our approach is competitive with native implementations and significantly outperforms such implementations on queries that involve aggregation.Comment: extended version of PVLDB pape

Theoretical framework of temporal databases.

by Lam Wing Hee.Thesis (M.Phil.)--Chinese University of Hong Kong, 1991.Bibliography: leaves [56]-59.List of Figures --- p.vAcknowledgements --- p.viChapter 1. --- Introduction --- p.1Chapter 1.1 --- Historical Data and Temporal Databases --- p.1Chapter 1.2 --- Valid Time and Transaction Time --- p.3Chapter 1.2.1 --- Snapshot Databases --- p.3Chapter 1.2.2 --- Rollback Databases --- p.4Chapter 1.2.3 --- Historical Databases --- p.6Chapter 1.2.4 --- Temporal Databases --- p.7Chapter 1.3 --- Literature Review --- p.8Chapter 1.3.1 --- Data Models --- p.9Chapter 1.3.2 --- Query Languages --- p.11Chapter 1.3.3 --- Logical Design --- p.13Chapter 2. --- The Temporal Relational Data Model --- p.14Chapter 2.1 --- The Temporal Relational Data Model - Informal Description --- p.14Chapter 2.2 --- The Temporal Relational Data Model - Formal Description --- p.15Chapter 2.2.1 --- Valid and Transaction Time Intervals --- p.16Chapter 2.2.2 --- "Attributes, Tuples and Temporal Relations" --- p.16Chapter 2.3 --- What is a Key in Temporal Relations? --- p.17Chapter 3. --- The Temporal Relational Algebra --- p.20Chapter 3.1 --- Operations in the Temporal Relational Algebra --- p.20Chapter 3.1.1 --- Union and Set Difference --- p.21Chapter 3.1.2 --- Selection --- p.21Chapter 3.1.3 --- Projection --- p.23Chapter 3.1.4 --- Join --- p.24Chapter 3.1.4.1 --- Natural Join --- p.25Chapter 3.2 --- Temporal Relational Algebra and TempSQL --- p.30Chapter 4. --- Classical Data Dependencies in Temporal Relations --- p.32Chapter 4.1 --- Functional Dependency in the Temporal Relational Model --- p.32Chapter 4.2 --- Multivalued Dependency in the Temporal Relational Model --- p.33Chapter 4.3 --- Relationship with Snapshot Data Dependencies --- p.34Chapter 4.4 --- Lossless Decomposition --- p.35Chapter 5. --- Asynchronous Dependency --- p.39Chapter 5.1 --- Asynchronous Dependency --- p.40Chapter 5.2 --- Asynchronous Normal Form --- p.41Chapter 5.3 --- Generalized Form of Data Dependency --- p.42Chapter 5.3.1 --- Embedded Implicational Dependency --- p.43Chapter 5.3.2 --- Algebraic Dependency --- p.45Chapter 5.4 --- Asynchronous Dependency versus Synchronous Dependency --- p.46Chapter 6. --- Conclusions --- p.48Chapter 6.1 --- Summary of the Thesis --- p.48Chapter 6.2 --- Unsolved Problems and Research Directions --- p.49Chapter 6.2.1 --- Equivalent Representations in the Temporal Relational Model --- p.49Chapter 6.2.2 --- The Notion of 'Completeness' of Temporal Query Languages --- p.50Chapter 6.2.3 --- Logical Basis for Temporal Data Models and Languages --- p.51Chapter 6.2.4 --- Other Temporal Dependencies --- p.51Chapter 6.2.5 --- Research Directions in Topics other than Theory --- p.52Appendix Proofs of Theorems --- p.53Bibliography --- p.5