Temporal Data Model Research Articles

A web-based architecture for Temporal OLAP

Commercial OLAP systems usually consider OLAP dimensions as static entities. In practice, dimension updates are often necessary in order to adapt the multidimensional database to changing requirements. Query languages are then needed to retrieve historical information from a data warehouse. Although many proposals addressing temporal OLAP exist, none supports full operation over the World Wide Web. After introducing a temporal data model supporting historical dimensions and fact table versioning, we present a three-tier architecture based on web services, SOAP and XML, allowing efficient querying over the web. In this architecture, XML metadata is stored at the application server in the form of XML documents containing the data warehouse structure. This allows addressing most of the requests (navigation through time, updates and queries) without accessing the data warehouse. In the second part of the paper, we present TOLAP-QL, an SQL-like temporal query language. We describe the syntax and semantics of the language, a sequel of its logic-based predecessor, TOLAP. We study query optimisation in temporal OLAP, and introduce a technique based on materialisation of historical views. Last, but not least, we present a system implementation, and a real-world case study, that shows that our proposal achieves good query performance for OLAP queries over large historical databases in a web environment.

RFID 데이터 스트림의 효율적인 필터링 기법

Radio Frequency Identification(RFID) technology is set to play an essential role in object tracking or supply chain management systems. New challenges for RFID data management are needed in the RFID applications. RFID data are generated quickly and automatically, and can be used for object tracking, or for real-time monitoring. These applications are mostly associated with the timestamps when the events happen. In this paper, we propose a temporal RFID data model to maintain the history of events and state changes and to monitor the states of RFID objects. Also we propose data filtering method of non active data based on temporal RFID data model. This data model involves essential basic operations for RFID data. We show increased query performance through the data filtering method of non active data.

A conceptual model for temporal data warehouses and its transformation to the ER and the object-relational models

The MultiDim model is a conceptual multidimensional model for data warehouse and OLAP applications. These applications require the presence of a time dimension to track changes in measure values. However, the time dimension cannot be used to represent changes in other dimensions. In this paper we introduce a temporal extension of the MultiDim model. This extension is based on research realized in temporal databases. We allow different temporality types: valid time, transaction time, and lifespan, which are obtained from source systems, and loading time, which is generated in the data warehouse. Our model provides temporal support for levels, attributes, hierarchies, and measures. For hierarchies we discuss different cases depending on whether the changes in levels or in the relationships between them must be kept. For measures, we give different scenarios that show the usefulness of the different temporality types. Further, since measures can be aggregated before being inserted into data warehouses, we discuss the issues related to different time granularities between source systems and data warehouses. We finish the paper presenting a transformation of the MultiDim model into the entity-relationship and the object-relational models.

An XML-based methodology for parametric temporal database model implementation

Parametric data model is one of dimensional data models. It defines attributes as functions, modeling a real world object into a single tuple in a database. Such one-to-one correspondence between an object in the real world and a tuple provides various advantages in modeling dimensional data, avoiding self-joins which frequently appear in temporal data models which fragment an object into multiple tuples. Despite its modeling advantages, it is impractical to implement the parametric data model on top of conventional database systems because of the data model’s variable attribute sizes. However, such implementation challenge can be resolved by XML because XML is flexible for data boundaries. In this paper, we present an XML-based implementation methodology for the parametric temporal data model. In our implementation, we develop our own XML storage called CanStoreX (Canonical Storage for XML) and build the temporal database system on top of the CanStoreX.

An introduction to the general temporal data model and the structured population event history register (SPEHR).

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.

Unsupervised pattern mining from symbolic temporal data

We present a unifying view of temporal concepts and data models in order to categorize existing approaches for unsupervised pattern mining from symbolic temporal data. In particular we distinguish time point-based methods and interval-based methods as well as univariate and multivariate methods. The mining paradigms and the robustness of many proposed approaches are compared to aid the selection of the appropriate method for a given problem. For time points, sequential pattern mining algorithms can be used to express equality and order of time points with gaps in multivariate data. For univariate data and limited gaps suffix tree methods are more efficient. Recently, efficient algorithms have been proposed to mine the more general concept of partial order from time points. For time interval data with precise start and end points the relations of Allen can be used to formulate patterns. The recently proposed Time Series Knowledge Representation is more robust on noisy data and offers an alternative semantic that avoids ambiguity and is more expressive. For both pattern languages efficient mining algorithms have been proposed.

A Mathematical Model for Temporal Data Mining In Estimation of Traffic Data over a Busy Area

A Mathematical Model for Temporal Data Mining In Estimation of Traffic Data over a Busy Area

A general temporal data model and the structured population event history register.

At this time there are 37 demographic surveillance system sites active in sub-Saharan Africa, Asia and Central America, and this number is growing continuously. These sites and other longitudinal population and health research projects generate large quantities of complex temporal data in order to describe, explain and investigate the event histories of individuals and the populations they constitute. This article presents possible solutions to some of the key data management challenges associated with those data. The fundamental components of a temporal system are identified and both they and their relationships to each other are given simple, standardized definitions. Further, a metadata framework is proposed to endow this abstract generalization with specific meaning and to bind the definitions of the data to the data themselves. The result is a temporal data model that is generalized, conceptually tractable, and inherently contains a full description of the primary data it organizes. Individual databases utilizing this temporal data model can be customized to suit the needs of their operators without modifying the underlying design of the database or sacrificing the potential to transparently share compatible subsets of their data with other similar databases. A practical working relational database design based on this general temporal data model is presented and demonstrated. This work has arisen out of experience with demographic surveillance in the developing world, and although the challenges and their solutions are more general, the discussion is organized around applications in demographic surveillance. An appendix contains detailed examples and working prototype databases that implement the examples discussed in the text.

An algebraic framework for temporal attribute characteristics

Most real-world database applications manage temporal data, i.e., data with associated time references that capture a temporal aspect of the data, typically either when the data is valid or when the data is known. Such applications abound in, e.g., the financial, medical, and scientific domains. In contrast to this, current database management systems offer preciously little built-in query language support for temporal data management. This situation persists although an active temporal database research community has demonstrated that application development can be simplified substantially by built-in temporal support. This paper's contribution is motivated by the observation that existing temporal data models and query languages generally make the same rigid assumption about the semantics of the association of data and time, namely that if a subset of the time domain is associated with some data then this implies the association of any further subset with the data. This paper offers a comprehensive, general framework where alternative semantics may co-exist. It supports so-called malleable and atomic temporal associations, in addition to the conventional ones mentioned above, which are termed constant. To demonstrate the utility of the framework, the paper defines a characteristics-enabled temporal algebra, termed CETA, which defines the traditional relational operators in the new framework. This contribution demonstrates that it is possible to provide built-in temporal support while making less rigid assumptions about the data and without jeopardizing the degree of the support. This moves temporal support closer to practical applications.

Managing Temporal Knowledge in Port Management Systems

Large ports need to deal with a number of disparate activities:the movement of ships, containers and other cargo, theloading and unloading of ships and containers, customs activities.As well as human resources, anchorages, channels, lighters,tugs, berths, warehouse and other storage spaces have to beallocated and released. The efficient management of a port involvesmanaging these activities and resources, managing theflows of money involved between the agents providing and usingthese resources, and providing management information.Many information systems will be involved.Many applications have to deal with a large amount of datawhich not only represent the perceived state of the real world atpresent, but also past and/or future states. These applicationsare not served adequately by today's computer managementand database systems. In particular, deletions and updates insuch systems have destructive semantics. This means that previousdatabase contents (representing previous perceived statesof the real world) cannot be accessed anymore.A review of how define temporal data models, based ongeneralizing a non-temporal data model in to a temporal one toimprove port management is presented. This paper describes apractical experiment which supports managing temporal dataalong with the corresponding prototype implementations.

A comparison of two approaches to utilizing XML in parametric databases for temporal data

The parametric data model captures an object in terms of a single tuple. This feature eliminates unnecessary self-join operations to combine tuples scattered in a temporal relation. Despite this advantage, this model is relatively difficult to implement on top of relational databases because the sizes of attributes are unfixed. Since data boundaries are not problematic in XML, XML can be an elegant solution to implement parametric databases for temporal data. There are two approaches to implementing parametric databases using XML: (1) a native XML database with XQuery engine, and (2) an XML storage with a temporal query language. To determine which approach is appropriate in parametric databases, we consider four questions: the effectiveness of XML in modeling temporal data, the applicability of XML query languages, the user-friendliness of the query languages, and system performances of two approaches. By evaluating the four questions, we show that the latter approach is more appropriate to utilizing XML in parametric databases.

Similarity-Based Retrieval of Temporal Specifications and its Application to the Retrieval of Multimedia Documents

In this paper, we describe a similarity-based retrieval framework for temporal information, such as multimedia presentations. We develop techniques that allow users to query and retrieve multimedia documents, based on their temporal content. For this purpose, we describe different temporal data models and a set of similarity metrics applicable for different retrieval tasks. We develop algorithms that efficiently compute these metrics and report on experiment results. We also develop algorithms that efficiently index temporal structures based on these measures and show that the proposed variant of multi dimensional scaling is efficient and provides high quality retrieval of temporal specifications.

A Prototype Temporal GIS for Multiple Spatio-Temporal Representations

Development of a temporal geographic information system (GIS) and spatio-temporal data modeling are key to incorporating time into geographic information science. This paper describes how to design and develop temporal GIS that will work with spatio-temporal data represented in various data models, and it introduces a prototype temporal GIS with a case study. In temporal GIS, the integration of multiple spatio-temporal representations is based on common spatial and temporal reference systems. In other words, a map window of temporal GIS visualizes spatio-temporal data valid at the same time within one spatial area. To achieve such visualization, separate data editing and query modules are required for each spatio-temporal data model (STDM). In the temporal query interface, after a user specifies a time, the system automatically hires correspondent modules to retrieve spatio-temporal data valid at that time. Besides temporal queries common to all STDMs, each module may provide additional temporal query capabilities specific to that STDM. In the case study, I implement a prototype temporal GIS for three STDMs. The examples of query and visualization, which use three datasets (census data, land use/land cover, and elevation data) demonstrate the prototype temporal GIS can integrate multiple temporal representations.

Literature review of spatio-temporal database models

Recent efforts in spatial and temporal data models and database systems have attempted to achieve an appropriate kind of interaction between the two areas. This paper reviews the different types of spatio-temporal data models that have been proposed in the literature as well as new theories and concepts that have emerged. It provides an overview of previous achievements within the domain and critically evaluates the various approaches through the use of a case study and the construction of a comparison framework. This comparative review is followed by a comprehensive description of the new lines of research that emanate from the latest efforts inside the spatio-temporal research community.

Handling Expiration of Multigranular Temporal Objects

A well-known problem of temporal databases is that the amount of stored data tends to increase very fast. Moreover, detailed data are useful when they are acquired but they often become less relevant after some time. In most cases, after a period of time only summarized data need to be kept, whereas detailed data expire and can be removed from the database. Multigranular temporal databases enhance the expressive power of temporal databases by supporting temporal attributes at different levels of detail. However, in existing approaches the level of detail of an attribute, that is its granularity, depends only on the attribute semantics and does not depend on how recent the attribute values are. This paper proposes an approach supporting the aggregation of different portions of the value of a temporal attribute at different levels of detail, and the deletion or the transfer to tertiary storage of old values at a given level of detail, in order to minimize disk storage occupancy. In the proposed multigranular temporal object-oriented data model, the expiration of attribute values at a given granularity can be specified, together with the action to take when data expire: either aggregation to a coarser granularity, or deletion of values, or both.

Data model for warehousing historical Web information

In this paper, we present a temporal web data model designed for warehousing historical data from World Wide Web (WWW). As the Web is now populated with large volume of information, it has become necessary to capture selected portions of web information in a data warehouse that supports further information processing such as data extraction, data classification, and data mining. Nevertheless, due to the unstructured and dynamic nature of Web, the traditional relational model and its temporal variants could not be used to build such a data warehouse. In this paper, we therefore propose a temporal web data model that represents web documents and their connectivities in the form of temporal web tables. To represent web data that evolve with time, a visible time interval is associated with each web document. To manipulate temporal web tables, we have defined a set of web operators with capabilities ranging from extracting WWW information into web tables, to merging information from different web tables. We further illustrate the use of our temporal web data model using some realistic motivating examples.

T-ODMG: an ODMG compliant temporal object model supporting multiple granularity management

In this paper we investigate some issues arising from the introduction of multiple temporal granularities in an object-oriented data model. Although issues concerning temporal granularities have been investigated in the context of temporal relational database systems, no comparable amount of work has been done in the context of object-oriented models. Moreover, the main drawback of the existing proposals is the lack of a formal basis—which we believe is essential to manage the inherent complexity of the object-oriented data model. In this paper, we define a comprehensive temporal object-oriented data model supporting multiple temporal granularities. We formally define the main notions of the data model such as types, legal values, classes, and objects. We address issues related to inheritance, type refinement, and substitutability. Finally, we describe the implementation of the presented model on top of an ODMG compliant DBMS.

Formalizing visual interaction with historical databases

Recent database applications are typically oriented towards a large set of non-expert users, and therefore, they need to be equipped with suitable interfaces facilitating the interaction with the system. Moreover, the incorporation of the time dimension in database systems is a desirable feature. Indeed, several temporal data models and the corresponding textual query languages have been proposed. However, there is a limited amount of research concerning the investigation of user-oriented languages for querying temporal databases. Our proposal addresses such a need. In particular, we propose a visual query environment, namely Temporal Visual Query Environment (TVQE) which provides an easier interaction of the user with temporal databases. The system adopts a diagrammatic representation of the database schema (including temporal classes and relationships) and a “graphical notebook” as interaction metaphor. In our approach, non-database experts are released from syntactical difficulties which are typical of textual languages, and they can easily express temporal queries by means of elementary graphical operations (e.g. click on a node label). Differently from many proposals in the field of visual query languages, the language underlying TVQE is provided with formal syntax and semantics. It is based on a minimal set of temporal graphical primitives (TGPs), which are defined on a Temporal Graph Model (TGM), with visual syntax and object-based semantics. In this paper we mainly concentrate on the formal aspects of TVQE, and provide some hints on the visual interaction mechanisms and implementation issues.

HMAP– A temporal data model managing intervals with different granularities and indeterminacy from natural language sentences

The granularity of given temporal information is the level of abstraction at which information is expressed. Different units of measure allow one to represent different granularities. Indeterminacy is often present in temporal information given at different granularities: temporal indeterminacy is related to incomplete knowledge of when the considered fact happened. Focusing on temporal databases, different granularities and indeterminacy have to be considered in expressing valid time, i.e., the time at which the information is true in the modeled reality. In this paper, we propose HMAP (The term is the transliteration of an ancient Greek poetical word meaning “day”.), a temporal data model extending the capability of defining valid times with different granularity and/or with indeterminacy. In HMAP, absolute intervals are explicitly represented by their start,end, and duration: in this way, we can represent valid times as “in December 1998 for five hours”, “from July 1995, for 15 days”, “from March 1997 to October 15, 1997, between 6 and 6:30 p.m.”. HMAP is based on a three-valued logic, for managing uncertainty in temporal relationships. Formulas involving different temporal relationships between intervals, instants, and durations can be defined, allowing one to query the database with different granularities, not necessarily related to that of data. In this paper, we also discuss the complexity of algorithms, allowing us to evaluate HMAP formulas, and show that the formulas can be expressed as constraint networks falling into the class of simple temporal problems, which can be solved in polynomial time.

Fuzzy association degree with delayed time in temporal data model

This paper presents an expression of the semantic proximity. Based on the temporal data model, a method of the temporal approximation is given. Using these concepts, this paper provides an evaluated method of fuzzy and dynamic association degree with delayed time and a superposition method of association degrees. Particularly, by means of the fuzzy and dynamic association degree, the connection between the weather data of two regions can be discovered.