Dataspace systems constitute a recent data management approach that supports better cooperation among autonomous and heterogeneous data sources with which the user is initially unfamiliar. A central idea is to gradually increase the user's knowledge about the contents, structures, and semantics of the data sources in the dataspace. Without this knowledge, the user is not able to make sophisticated queries. The dataspace systems proposed so far are usually application specific. In contrast, our idea in this paper is to develop an application‐independent extensible markup language (XML) dataspace system with versatile facilities. Unlike the other proposed dataspace systems, we show that it is possible to build an interface based on conventional visual tools in terms of which the user can satisfy his or her sophisticated information needs. In our system, the user does not need to master programming techniques nor the XML syntax, which provides a good starting point for its declarative use.

Abstract Data cubes for OLAP (On‐Line Analytical Processing) often need to be constructed from data located in several distributed and autonomous information sources. Such a data integration process is challenging due to semantic, syntactic, and structural heterogeneity among the data. While XML (extensible markup language) is the de facto standard for data exchange, the three types of heterogeneity remain. Moreover, popular path‐oriented XML query languages, such as XQuery, require the user to know in much detail the structure of the documents to be processed and are, thus, effectively impractical in many real‐world data integration tasks. Several Lowest Common Ancestor (LCA)‐based XML query evaluation strategies have recently been introduced to provide a more structure‐independent way to access XML documents. We shall, however, show that this approach leads in the context of certain—not uncommon—types of XML documents to undesirable results. This article introduces a novel high‐level data extraction primitive that utilizes the purpose‐built Smallest Possible Context (SPC) query evaluation strategy. We demonstrate, through a system prototype for OLAP data cube construction and a sample application in informetrics, that our approach has real advantages in data integration.

Abstract In contemporary query languages, the user is responsible for navigation among semantically related data. Because of the huge amount of data and the complex structural relationships among data in modern applications, it is unrealistic to suppose that the user could know completely the content and structure of the available information. There are several query languages whose purpose is to facilitate navigation in unknown structures of databases. However, the background assumption of these languages is that the user knows how data are related to each other semantically in the structure at hand. So far only little attention has been paid to how unknown semantic associations among available data can be discovered. We address this problem in this article. A semantic association between two entities can be constructed if a sequence of relationships expressed explicitly in a database can be found that connects these entities to each other. This sequence may contain several other entities through which the original entities are connected to each other indirectly. We introduce an expressive and declarative query language for discovering semantic associations. Our query language is able, for example, to discover semantic associations between entities for which only some of the characteristics are known. Further, it integrates the manipulation of semantic associations with the manipulation of documents that may contain information on entities in semantic associations.

Abstract In our query language introduced in Part I (Niemi & Jämsen, in press) the user can formulate queries to find out (possibly complex) semantic relationships among entities. In this article we demonstrate the usage of our query language and discuss the new applications that it supports. We categorize several query types and give sample queries. The query types are categorized based on whether the entities specified in a query are known or unknown to the user in advance, and whether text information in documents is utilized. Natural language is used to represent the results of queries in order to facilitate correct interpretation by the user. We discuss briefly the issues related to the prototype implementation of the query language and show that an independent operation like Rho (Sheth et al., 2005; Anyanwu & Sheth, 2002, 2003), which presupposes entities of interest to be known in advance, is exceedingly inefficient in emulating the behavior of our query language. The discussion also covers potential problems, and challenges for future work.

Abstract Multidimensional data analysis or On‐line analytical processing (OLAP) offers a single subject‐oriented source for analyzing summary data based on various dimensions. We demonstrate that the OLAP approach gives a promising starting point for advanced analysis and comparison among summary data in informetrics applications. At the moment there is no single precise, commonly accepted logical/conceptual model for multidimensional analysis. This is because the requirements of applications vary considerably. We develop a conceptual/logical multidimensional model for supporting the complex and unpredictable needs of informetrics. Summary data are considered with respect of some dimensions. By changing dimensions the user may construct other views on the same summary data. We develop a multidimensional query language whose basic idea is to support the definition of views in a way, which is natural and intuitive for lay users in the informetrics area. We show that this view‐oriented query language has a great expressive power and its degree of declarativity is greater than in contemporary operation‐oriented or SQL (Structured Query Language)‐like OLAP query languages.

Next generation information systems (NGISs) have to support the manipulation of data-oriented, behavioral and deductive aspects of application domains. Many modeling methods, e.g. UML and other object-oriented modeling methods offer primitives for modeling data-oriented and behavioral aspects but they do not support the modeling of deductive aspects. In addition, NGISs may be implemented by several separate software/database systems that are based on different paradigms. Therefore it is not appropriate to use such a modeling method which is based on one paradigm. In NGISs it is essential to integrate the value-oriented approach with the object-oriented approach. We develop a relational deductive object-oriented modeling (RDOOM) approach for NGISs. Our goal is to combine into one modeling method, the navigation power of the relational model, the modeling power of the object-orientation and the inference power of the deductive (logical) framework. It is obvious that in NGISs complex and large specifications have to be embedded in application-specific concepts and structures that are defined beforehand for users to facilitate their query formulation. The detection and specification of application-specific concepts and structures means a new challenge for analysis methods. We show that on the basis of the primitives of RDOOM it is possible to represent this kind of application-specific information in a natural way. It is also necessary that the appropriateness and adequacy of application-specific concepts and structures can be tested before their expensive design and implementation phases. For the definition of these concepts and structures a diagrammatic representation typical of many modeling methods is not sufficient. Rather, a precise and executable representation is needed. Especially the complex and large derivation rules behind deductive concepts cannot be expressed precisely with diagrammatic representations. We develop set-theoretical representations for our primitives. The precise representation of the result of systems analysis also gives a more substantial starting point for the design and implementation of NGISs.

We present a deductive data model for conceptbased query expansion.It ]s based on three abstraction levels: the conceptual, linguistic and occurrence levels.Concepts and relationships among them are represented at the conceptual level.The expression level represents natural language expressions for concepts.Each expression has one or more matchmg models at the occurrence level.The models specify the matching of the expression in database Indices built in varying ways.The data model supports a concept-based query expansion and formulation tool, the ExpansionTool, for heterogeneous IR system envirom ments, Expansion is controlled by adjustable matching reliability.

Complexdocuments are used in many environments, e.g., information retrieval (IR).Such documents contain subdocuments, which may contain further subdocuments, etc. Powerful tools are needed to facilitate their retrieval, restructuring, and analysis.Existing IR systems are poor in complex document restructuring and data aggregation.However, in practice, IR system users would often want to obtain aggregation information on subdocuments of complex documents.In this paper we address this problem and provide a truly declarative and powerful interface for the users.Our interface is based on the non-fkst-normal-form (NF2) relational model.It allows intuitive and systematic modeling of complex documents.

Modern fact databases contain abundant data classified through several classifications. Typically, users must consult these classifications in separate manuals or files, thus making their effective use difficult. Contemporary database systems do little to support deductive use of classifications. In this study we show how deductive data management techniques can be applied to the utilization of data value classifications. Computation of transitive class relationships is of primary importance here. We define a representation of classifications which supports transitive computation and present an operation-oriented deductive query language tailored for classification-based deductive information retrieval. The operations of this language are on the same abstraction level as relational algebra operations and can be integrated with these to form a powerful and flexible query language for deductive information retrieval. We define the integration of the operations and demonstrate the usefulness of the language in terms of several sample queries. © 1993 John Wiley & Sons, Inc.

Article Data conversion, aggregation and deduction for advanced retrieval from the heterogeneous fact databases Share on Authors: Kalervo Järvelin Dept. of Information Studies, University of Tampere, P.O.Box 607 SF-33101 TAMPERE, Finland Dept. of Information Studies, University of Tampere, P.O.Box 607 SF-33101 TAMPERE, FinlandView Profile , Timo Niemi Dept. of Computer Science, University of Tampere, P.O.Box 607 SF-33101 TAMPERE, Finland Dept. of Computer Science, University of Tampere, P.O.Box 607 SF-33101 TAMPERE, FinlandView Profile Authors Info & Claims SIGIR '91: Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrievalSeptember 1991 Pages 173–182https://doi.org/10.1145/122860.122877Online:01 September 1991Publication History 2citation356DownloadsMetricsTotal Citations2Total Downloads356Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

A Prolog-based experimental system for relational databases that is not defined from the viewpoint of any specific relational topic is proposed. The idea is that the experimental system can be used in many different contexts such as query optimization, data restructuring and database design. The definition is based entirely on the theoretical foundations of the relational model. The experimental system offers a well-defined environment for studying how other systems can be integrated with relational databases. The use of the experimental system in the context of different approaches to deductive databases is considered.>