Modeling and Querying Temporal Data

In general, databases store current data. However,the capability to maintain temporal data is a crucial requirement for many organizations and provides the base for organizational intelligence. A temporal database maintains time-varying data, that is, past, present, and future data. In this chapter, we focus on the relational data model and address the subtle issues in modeling and designing temporal databases. A common approach to handle temporal data within the traditional relational databases is the addition of time columns to a relation. Though this appears to be a simple and intuitive solution, it does not address many subtle issues peculiar to temporal data, that is, comparing database states at two different time points, capturing the periods for concurrent events and accessing times beyond these periods, handling multi-valued attributes, coalescing and restructuring temporal data, and so forth, [Gadia 1988, Tansel and Tin 1997]. There is a growing interest in temporal databases. A first book dedicated to temporal databases [Tansel at al 1993] followed by others addressing issues in handling time-varying data [Betini, Jajodia and Wang 1988, Date, Darwen and Lorentzos 2002, Snodgrass 1999].

Data model for warehousing historical Web information

DNA microarray technology provides a powerful vehicle for exploring biological processes on a genomic scale. Machine-learning approaches such as association rule mining (ARM) have been proven very effective in extracting biologically relevant associations among different genes. Despite of the usefulness of ARM, time relations among associated genes cannot be modeled with a standard ARM approach, though temporal information is critical for the understanding of regulatory mechanisms in biological processes. Sequential rule mining (SRM) methods have been proposed for mining temporal relations in temporal data instead. Although successful, existing SRM applications on temporal microarray data have been exclusively designed for in vitro experiments in yeast and none extension to in vivo data sets has been proposed to date. Contrary to what happen with in vitro experiments, when dealing with microarray data derived from humans or animals the “subject variability” is the main issue to address, so that databases include multiple sequences instead of a single one. A wide variety of SRM approaches could be used to handle with these particularities. In this study, we propose an adaptation of the particular SRM method “CMRules” to extract sequential association rules from temporal gene expression data derived from humans. In addition to the data mining process, we further propose the validation of extracted rules through the integration of results along with external resources of biological knowledge (functional and pathway annotation databases). The employed data set consists on temporal gene expression data collected in three different time points during the course of a dietary intervention in 57 subjects with obesity (data set available with identifier GSE77962 in the Gene Expression Omnibus repository). Published by Vink [1], the original clinical trial investigated the effects on weight loss of two different dietary interventions (a low-calorie diet or a very low-calorie diet). In conclusion, the proposed method demonstrated a good ability to extract sequential association rules with further biological relevance within the context of obesity. Thus, the application of this method could be successfully extended to other longitudinal microarray data sets derived from humans.

In ubiquitous environments, many applications need to process data with time and space dimensions. Because of this, there is growing attention not only on gathering spatiotemporal data in ubiquitous environments, but also on processing such data in databases. In order to obtain the full benefits from spatiotemporal data, we need a data model that naturally expresses the properties of spatiotemporal data. In this paper, we introduce three spatiotemporal data models extended from temporal data models. The main goal of this paper is to determine which data model is less complex in the spatiotemporal context. To this end, we compare their query languages in the complexity aspect because the complexity of a query language is tightly coupled with its underlying data model. Throughout our investigations, we show that it is important to intertwine space and time dimensions and keep one-to-one correspondence between an object in the real world and a tuple in a database in order to naturally express queries in ubiquitous applications.

We address the key issues of temporal data modeling and temporal integrity constraints in the temporal extensions to the relational data model in attribute and tuple time stamping. These issues include representation of temporal data, temporal grouping identifiers, primary keys of temporal relations and other forms of temporal integrity constraints. We extend the traditional integrity constraints to the temporal domain. In tackling subtle issues temporality creates, we conceptualize that a temporal integrity constraint has a data and a temporal (time) component. The time component of a temporal integrity constraint limits the attribute values to the lifespan of an object (tuple). We also generalize single state integrity constraints to multi-state temporal integrity constraints and apply them to existential and referential integrity. We provide algorithms for enforcing the two types of temporal integrity constraints.

In this paper we examine the semantics and develop constructs for temporal data independent of any traditional data model, such as the relational or network data models. Unlike many other works which extend existing models to support temporal data, our purpose is to characterize the properties of temporal data and operators over them without being influenced by traditional models which were not specifically designed to model temporal data. We develop data constructs that represent sequences of temporal values, identify their semantic properties, and define operations over these structures.

In order to make the fuzzy information more accurate, Vague Set is used to represent the fuzzy attribute of the database in this paper. Each data and tuple in the database based on Vague Set can be reflected from the three sides of true, false and the unknown extent. For illustration, a vague relationship instance teacher1 is utilized to show the basic theory of vague relation. Results show that if the database model based on Vague Set and the fuzzy relational data model are compared, the former has a more fuzzy ability to express. The further expansion of vague database model which is temporal database based on Vague Set is proposed. In order to better explain vague temporal data, a time dimension ‘duty period’ is added into table teacher1, which constitutes vague temporal relation teacher2. The study lays the foundation for the further research into data model, data theory, and database management system of vague temporal database.

On handling time-varying data in the relational data model

There has been much attention given recently to the task of finding interesting patterns in temporal databases. Since there are so many different approaches to the problem of discovering temporal patterns, we first present a characterization of different discovery tasks and then focus on one task of discovering interesting patterns of events in temporal sequences. Given an (infinite) temporal database or a sequence of events one can, in general, discover an infinite number of temporal patterns in this data. Therefore, it is important to specify some measure of interestingness for discovered patterns and then select only the patterns interesting according to this measure. We present a probabilistic measure of interestingness based on unexpectedness, whereby a pattern P is deemed interesting if the ratio of the actual number of occurrences of P exceeds the expected number of occurrences of P by some user defined threshold. We then make use of a subset of the propositional, linear temporal logic and present an efficient algorithm that discovers unexpected patterns in temporal data. Finally, we apply this algorithm to synthetic data, UNIX operating system calls, and Web logfiles and present the results of these experiments.KeywordsTemporal LogicInteresting PatternLinear Temporal LogicInput StringInterestingness MeasureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In the public goods game, players can be classified into different types according to their participation in the game. It is an important issue for economists to be able to measure players’ strategy changes over time which can be considered as concept drift. In this study, we present a method for measuring changes in items’ cluster membership in temporal data. The method consists of three steps in the first step, the temporal data will be transformed into a discrete series of time points then each time point will be clustered separately. In the last step, the items’ membership in the clusters is compared with a reference of behaviour to determine the amount of behavioural change in each time point. Different external cluster validity indices and area under the curve are used to measure these changes. Instead of different cluster label comparison, we use these indices a new way to compare between clusters and reference points. In this study, three categories of reference of behaviours are used 1- first time point, 2- previous time pint and 3- the general overall behaviour of the items. For the public goods game, our results indicate that the players are changing over time but the change is smooth and relatively constant between any two time points.

Attaching Date and Time to varying data plays a definite role in representing a dynamic domain and resources on the database systems. The conventional database stores current data and can only represent the knowledge in static sense. Time-varying data representations in relational framework are well described in literature. In this paper we propose an approach for modeling First Normal Form (1NF) and interval-based temporal database over conventional Database Management Systems (DBMS). We choose 1NF approach with interval-based temporal data model for the easily implementation in relational framework as well as to provide the proposed temporal data representation with the modeling and querying power of 1NF relational data model.

In the field of databases, time management at different levels of granularity has been an issue for several years, for instance when dealing with clinical information from different databases using different time units, dealing with natural language expressions, or when dealing with temporal uncertainty. A temporal data model is proposed to manage the temporal aspect of data, presented at various and mixed levels of granularity. The concept of temporal assertions shapes the entire temporal information. The model provides a temporal dimension to the data by using intervals that can be specified at different granularities. The model supports a three-valued logic, where True, False and Undefined are the truth values. The temporal data model allows to manage some degrees of uncertainty when establishing temporal relationships between intervals or between temporal assertions, expressed at different granularities. The logical connectives and quantifiers can manage each of the three truth-values. We applied the temporal data model by implementing an object-oriented database system for managing follow-up clinical data from patients who underwent percutaneous transluminal coronary angioplasty.

A database management system (DBMS) should provide capabilities for defining and enforcing certain integrity constraints that must hold for the data recorded in the database. These constraints are derived from the semantics of the data and from the rules of the system under study (SuS) that database represents. Every DBMS is based on a data model that provides the means to achieve data abstraction and to express database schema. Each data model has a specific set of integrity constraints types. Some of constraint types are common for several data models and some of them are typical for a data model. Beyond well-known constraint types there are more complex ones that are difficult to express and enforce within the data model. In this paper we deal with the extended tuple constraint (ETC) type. It is the extension of tuple constraint that is one of constraint types of relational data model. In this paper we explain the usage of extended tuple constraint in relational data model and its implementation in relational DBMSs. XML data model does not recognize ETC type. We specify the extended tuple constraint in XML data model and propose two techniques for implementation of ETC in XML DBMSs. Each of them depends on the characteristics of a selected XML DBMS. We use XQuery functions in eXist DBMS, and triggers in Sedna DBMS.

There are some 37 demographic surveillance system sites active in sub-Saharan Africa, Asia and Central America. These sites, and other longitudinal population and health research projects, generate data over time in order to describe and explain the event histories of individuals and the populations they constitute. This note addresses key data management challenges presented by such complex temporal data-gathering efforts. Ideas supporting a standard definition for temporal population data, and a standard design for temporal databases to improve management of longitudinal population data, are presented and briefly discussed.

Indexing transaction time databases