This article focuses on testing a path-oriented querying approach to hierarchical data in relational databases. The authors execute a user study to compare the path-oriented approach and traditional SQL from two perspectives: correctness of queries and time spent in querying. They also analyze what kinds of errors are typical in path-oriented SQL. Path-oriented query languages are popular in the context of object-orientation and XML. However, relational databases are the most common paradigm for storing data and SQL is most common for manipulating data. When querying hierarchical data in SQL, the user must specify join conditions explicitly between hierarchy levels. Path-oriented SQL is a new alternative for expressing hierarchical queries in relational databases. In the authors' study, the users spent significantly less time in writing path-oriented SQL queries and made fewer errors in query formulation.

Data-centric workflows focus on how the data is transferred between processes and how it is logically stored. In addition to traditional workflow analysis, these can be applied to monitoring, tracing, and analyzing data in processes and their mutual relationships. In many applications, e.g. manufacturing, the tracing of products thorough entire lifecycle is becoming more and more important. In the present paper we define the traceability graph that involves a framework for data that adapts to different levels of precision of tracing. Advanced analyzing requires modeling of data in processes and methods for accumulating resources and emissions thorough the lifecycle of products. The traceability graph enables tracing and accumulation of resources, emissions and other information associated with products. The traceability graph is formally defined by set theory that is an established and exact specification method.

Modeling of information systems is typically based on conceptual (intensional) primitives abstracted from a Universe of Discourse. Among different relationships the hierarchical is-a and part-of relationships play a central role in modeling. Traditionally these are viewed as separated structures. However, there are approaches that aim to view them thorough one intensional containment relationship among concepts. In this paper, we seek a solution for this philosophically hard question. In other words, we aim to find one universal relationship at the intensional level that covers both the is-a and part-of relationships. The study is based on the integration of two established theory – Kauppi's concept theory that is an intensional theory and Mereology that is a theory of parts and wholes at the extensional level.

XML retrieval provides a focused access to the relevant content of documents. However, in evaluation, full document retrieval has appeared competitive to focused XML retrieval. We analyze the density of relevance in documents, and show that in sparsely relevant documents focused retrieval performs better, whereas in densely relevant documents the performance of focused and document retrieval is equal.

In the hierarchical XML structure, the ancestors form the context of an XML element. The process of taking element's context into account in element scoring is called contextualization. The aim of this paper is to separate different granularity levels and test the effect of contextualization on these levels.

A general re-weighting method, called contextualization, for more efficient element ranking in XML retrieval is introduced. Re-weighting is based on the idea of using the ancestors of an element as a context: if the element appears in a good context -- good interpreted as probability of relevance -- its weight is increased in relevance scoring; if the element appears in a bad context, its weight is decreased. The formal presentation of contextualization is given in a general XML representation and manipulation frame, which is based on utilization of structural indices. This provides a general approach independent of weighting schemas or query languages.Contextualization is evaluated with the INEX test collection. We tested four runs: no contextualization, parent, root and tower contextualizations. The contextualization runs were significantly better than no contextualization. The root contextualization was the best among the re-weighted runs.

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.