Temporal Database Management System Research Articles

A lightweight and efficient temporal database management system in TDSQL

Driven by the recent adoption of temporal expressions into SQL:2011, extensions of temporal support in conventional database management systems (a.b.a. DBMSs) have re-emerged as a research hotspot. In this paper, we present a lightweight yet efficient built-in temporal implementation in Tencent's distributed database management system, namely TDSQL. The novelty of TDSQL's temporal implementation includes: (1) a new temporal data model with the extension of SQL:2011, (2) a built-in temporal implementation with various optimizations, which are also applicable to other DBMSs, and (3) a low-storage-consumption in which only data changes are maintained. For the repeatability purpose, we elaborate the integration of our proposed techniques into MySQL. We conduct extensive experiments on both real-life dataset and synthetic TPC benchmarks by comparing TD-SQL with other temporal databases. The results show that TDSQL is lightweight and efficient.

Extending the Kernel of a Relational DBMS with Comprehensive Support for Sequenced Temporal Queries

Many databases contain temporal, or time-referenced, data and use intervals to capture the temporal aspect. While SQL-based database management systems (DBMSs) are capable of supporting the management of interval data, the support they offer can be improved considerably. A range of proposed temporal data models and query languages offer ample evidence to this effect. Natural queries that are very difficult to formulate in SQL are easy to formulate in these temporal query languages. The increased focus on analytics over historical data where queries are generally more complex exacerbates the difficulties and thus the potential benefits of a temporal query language. Commercial DBMSs have recently started to offer limited temporal functionality in a step-by-step manner, focusing on the representation of intervals and neglecting the implementation of the query evaluation engine. This article demonstrates how it is possible to extend the relational database engine to achieve a full-fledged, industrial-strength implementation of sequenced temporal queries, which intuitively are queries that are evaluated at each time point. Our approach reduces temporal queries to nontemporal queries over data with adjusted intervals, and it leaves the processing of nontemporal queries unaffected. Specifically, the approach hinges on three concepts: interval adjustment , timestamp propagation , and attribute scaling . Interval adjustment is enabled by introducing two new relational operators, a temporal normalizer and a temporal aligner, and the latter two concepts are enabled by the replication of timestamp attributes and the use of so-called scaling functions. By providing a set of reduction rules, we can transform any temporal query, expressed in terms of temporal relational operators, to a query expressed in terms of relational operators and the two new operators. We prove that the size of a transformed query is linear in the number of temporal operators in the original query. An integration of the new operators and the transformation rules, along with query optimization rules, into the kernel of PostgreSQL is reported. Empirical studies with the resulting temporal DBMS are covered that offer insights into pertinent design properties of the article's proposal. The new system is available as open-source software.

TempoXML: Nested bitemporal relationship modeling and conversion tool for fuzzy XML

The importance of incorporating time in databases has been well realized by the research community. Accordingly, temporal databases have been extensively studied by researchers. The main idea is to add a time or temporal dimension to the model and then tag data elements with time in order to keep all values instead of only the last one, and hence allow for time driven queries. This way it becomes possible to retrieve various values of the same element. This leads for better knowledge discovery and trend analysis by looking back into the history to predict for the future. Unfortunately, one disadvantage of the temporal database management system is that it has not been commercialized. The work described in this paper reflects our effort to demonstrate the power and effectiveness of the temporal dimension once it is well integrated into databases. We decided on XML (eXtensible Markup Language) as the underlying data model. The motivation is twofold. First XML is a defacto standard for data exchange; we have already demonstrated the power of XML in our other work described in the literature. Second, nested bitemporal databases form one interesting type of temporal databases. Thus, our purpose is to suggest an automated system that converts a nested bitemporal database to a corresponding fuzzy XML database. Fuzzy query model has been implemented as part of the proposed framework in order to provide flexibility to a wide rang of end users willing to access the database. The implemented temporal operators are database content independent. Fuzzy elements are capable of having different membership functions and varying number of linguistic variables. We have proposed a scheme for determining membership function parameters.

Memory Storage Issues of Temporal Database Applications on Relational Database Management Systems

Problem statement: Many existing database applications manage time-varying data. These database applications are referred to as temporal databases or time-oriented database applications that are considered as repositories of time-dependent data. Many proposals have been introduced for developing time-oriented database applications, some of which suggest building support for Temporal Database Management Systems (TDBMS) on top of existing non-temporal DBMSs, while others suggest modifying the models of exiting DBMSs or building TDBMS from scratch. Approach: This study addressed several issues concerning developing a technique that enables database designers to understand the way in which time-varying behavior can be modeled and mapped into tabular form. Results: Conventional DBMSs do not have the capability to record and process time-varying aspects of the real world. With growing sophistication of DBMS applications, the lack of temporal support in conventional DBMS raises serious problems when used to develop temporal database. The technique of understanding how to think about time and represent it in formal systems is the topic of this study. We examined how to implement time-varying application in the SQL structured query language by introducing temporal data concepts that need to be simulated in DBMSs which lack temporal supports. We proposed a temporal data model that combines the features of previous temporal models and that reduces the cost of memory storage. Conclusion: We proposed a technique for implementing temporal database on top of exiting non-temporal DBMS. This technique includes five main areas. These areas are temporal database conceptual design, temporal database logical design, integrity constraints preventions in temporal database, modifying and querying temporal database. We proposed a data model for the temporal database based on the data models which are discussed in literature.

Main Memory-Based Algorithms for Efficient Parallel Aggregation for Temporal Databases

The ability to model the temporal dimension is essential to many applications. Furthermore, the rate of increase in database size and stringency of response time requirements has out-paced advancements in processor and mass storage technology, leading to the need for parallel temporal database management systems. In this paper, we introduce a variety of parallel temporal aggregation algorithms for the shared-nothing architectures these algorithms are based on the sequential Aggregation Tree algorithm. We are particularly interested in developing parallel algorithms that can maximally exploit available memory to quickly compute large-scale temporal aggregates without intermediate disk writes and reads. Via an empirical study, we found that the number of processing nodes, the partitioning of the data, the placement of results, and the degree of data reduction effected by the aggregation impacted the performance of the algorithms. For distributed result placement, we discovered that Greedy Time Division Merge was the obvious choice. For centralized results and high data reduction, Pairwise Merge was preferred for a large number of processing nodess for low data reduction, it only performed well up to 32 nodes. This led us to a centralized variant of Greedy Time Division Merge which was best for the remaining cases. We present a cost model that closely predicts the running time of Greedy Time Division Merge.

An Intelligent Query Language for Temporal Databases

Temibase is an intelligent temporal database management system which has been developed by integrating Knowledge Engineering techniques with temporal relational database management systems and this system has a special subsystem to perform temporal reasoning. The main difference between commercial database systems and TEMIBASE is its ability to perform temporal data management and temporal reasoning. In this paper, we explain the query language ETSQL (Extended Temporal Structured Query Language) which has been designed for querying TEMIBASE. ETSQL has been developed by extending the commercial query language SQL in which interval and instant operations are provided as extension. Moreover, this language provides special constructs for invoking the temporal reasoning tasks forecasting, explanation, planning and learning new rules.

A coherent architecture for a temporal object data base management system

The last decade has witnessed the emergence of practical general-purpose data base management systems (DBMS) as “state of the art” technology. In consequence, these systems are now widely used in a variety of application environments. However, many such systems were designed to capture current data, i.e., a snapshot of reality, but are insufficient for those applications in which past or future data are also required. Even so, there has been little effort to design temporal DBMS, and currently there are no prominent commercial temporal DBMS. This article identifies, explores, and provides an approach to designing a coherent architecture for a temporal object DBMS (TODBMS). It specifically involves the design, development, and implementation of TODBMS. We introduce the basic features of our system and provide an overview of the functionalities supported. In addition, we identify the major components of TODBMS and define the types of services each component should offer, as well as common abstractions and interfaces for these components. Application environments include management and geographic information systems, data base visualization, and robotics.

Handling shared resources in a temporal data base management system

An important component of planning involves keeping track of what various agents intend to do and recognizing situations in which certain planned-for activities are likely to interfere with one another. In planning situations involving several agents participating in a cooperative endeavor, this process can be automated to a high degree thereby facilitating coordination between agents. The more difficult aspects of planning, how to choose what activities to perform and what to do when those choices lead to problems, are left to the agents themselves. This paper demonstrates how the use of an interactive temporal data base management system can direct the inter-agent communication necessary to coordinate the use of shared resources and deal with complex scheduling problems. It is argued that much of the paper work normally associated with routine office transactions can be eliminated through the use of this temporal data base.

Performance evaluation of a temporal database management system

A prototype of a temporal database management system was built by extending Ingres. It supports the temporal query language TQuel, a superset of Quel, handling four types of database static, rollback, historical and temporal. A benchmark set of queries was run to study the performance of the prototype on the four types of databases. We analyze the results of the benchmark, and identify major factors that have the greatest impact on the performance of the system. We also discuss several mechanisms to address the performance bottlenecks we encountered.

Handling shared resources in a temporal data base management system

This paper presents an approach to managing shared resources through the use of a temporal data base and special purpose reason maintenance system. All resource requests are processed through a central data base management system that assists employees in scheduling events to take advantage of available resources. A temporal data base is used to keep track of the condition and use of these resources. The system assists in resolving conflicts between employees competing for the same resources and detects when a change in the condition of a resource selected for a particular task makes it unsuitable for its intended purpose.