Temporal Database

Abstract: Time is one of the most difficult aspects to handle in real world applications such as database systems. Relational database management systems proposed by Codd offer very little built-in query language support for temporal data management. The model itself ...

In conventional databases, the amount of data typically reaches a certain level and is then relatively stable. In databases supporting transaction time, old data are retained, and the amount of data is ever growing. Even with continued advances in mass storage technology, vacuuming (ie, deletion or off-line storage of data) will eventually be necessary. At the same time, the fundamental principle, that history cannot be changed, of transaction time databases must be obeyed. This paper provides a framework for vacuuming ...

Temporal data management is recognized concept of managing data that changes over time. Traditionally, a temporal data management system (TDMS) was considered separate system, i.e. an extension of the traditional relational database management system (RDBMS). Recently a non-relational database, called NoSQL, has become quite popular for scalability and storage of big data. Even with this new approach for managing data, temporal data management is still a work in progress. This paper provides an overview of temporal data management and NoSQL databases. Also, temporal data management is discussed within the context of NoSQL databases. This paper concludes with observations and suggestions for future research.

Most existing natural language database interfaces (NLDBs) were designed to be used with database systems that provide very limited facilities for manipulating time-dependent data, and they do not support adequately temporal linguistic mechanisms (verb tenses, temporal adverbials, temporal subordinate clauses, etc.). The database community is becoming increasingly interested in temporal database systems, that are intended to store and manipulate in a principled manner information not only about the present, but also about the past and future. When interfacing to temporal databases, supporting temporal linguistic mechanisms becomes crucial. We present a framework for constructing natural language interfaces for temporal databases (NLTDBs), that draws on research in tense and aspect theories, temporal logics, and temporal databases. The framework consists of a temporal intermediate representation language, called TOP, an HPSG grammar that maps a wide range of questions involving temporal mechanisms to appropriate TOP expressions, and a provably correct method for translating from TOP to TSQL2, TSQL2 being a recently proposed temporal extension of the SQL database language. This framework was employed to implement a prototype NLTDB using ALE and Prolog.

Related to the problem of temporal database design, we can design the relational target model from the TimeER model. Extraction of the TimeER model from a relational model is called reverse engineering of the relational model. Solving this problem will facilitate an upgraded temporal information system. That means we will investigate a conceptual model which is used to design the temporal relational model. This approach of the extraction is based on the characteristics of the set of attributes, the primary key, and the set of foreign keys of the relational schema in the temporal relational model. Thereby, we propose conversion algorithms relating to the identification of the components existing within the TimeER model respectively.

Migrating applications from conventional to temporal database management technology has received scant mention in the research literature. This paper formally defines three increasingly restrictive notions of upward compatibility which capture properties of a temporal SQL with respect to conventional SQL that, when satisfied, provide for a smooth migration of legacy applications to a temporal system. The notions of upward compatibility

Statistical database management systems keep raw, elementary and/or aggregated data and include query languages with facilities to calculate various statistics from this data. In this article we examine statistical database query languages with respect to the criteria identified and taxonomy developed in Ozsoyoglu and Ozsoyoglu (1985b). The criteria include statistical metadata and objects, aggregation features and interface to statistical packages. The taxonomy of statistical database query languages classifies them with respect to the data model used, the type of user interface and method of implementation. Temporal databases are rich sources of data for statistical analysis. Aggregation features of temporal query languages, as well as the issues in calculating aggregates from temporal data, are also examined.

Verbal and compositional lexical aspect provide the underlying temporal structure of events. Knowledge of lexical aspect, e.g., (a)telicity, is therefore required for interpreting event sequences in discourse (Dowty, 1986; Moens and Steedman, 1988; Passoneau, 1988), interfacing to temporal databases (Androutsopoulos, 1996), processing temporal modifiers (Antonisse, 1994), describing allowable alternations and their semantic effects (Resnik, 1996; Tenny, 1994), and selecting tense

In this paper we study the ramification problem in the setting of temporal databases. Standard solutions from the literature on reasoning about action are inadequate because they rely on the assumption that fluents persist, and because actions have effects on the next situation only. In this paper we provide a solution to the ramification problem based on an extension of the situation calculus and the work of McCain and Turner. We present a tool with a graphical user interface for addressing the ramification problem in temporal databases.

Abstract: Time is one of the most difficult aspects to handle in real world applications such as database systems. Relational database management systems proposed by Codd offer very little built-in query language support for temporal data management. The model itself ...