XPath has been established as the de facto standard for searching data items from hierarchical XML structures. Due to its popularity and compact path expressions, XPath has also been recognized as a query language candidate for graph databases where the structure does not follow a hierarchical order. Graph databases are based on graph theory and the data are organized accordingly. Among different types of graphs, property graphs have gained special interest because they allow data associated with edges as well as vertices, reflecting that edges represent relationships and relationships are generally allowed to have properties, just like entities. Earlier proposals to apply XPath to graph databases do not allow manipulation of the properties of edges in a property graph. The present study focuses on this issue. We show how XPath can be applied to full-scale property graphs. This requires a novel mapping of XPath primitives to the primitives of property graphs. Based on this mapping, we define graph-based semantics for XPath by regular path queries, an established logical approach for querying vertices and edges.

The multi-tiered shareholder identification and registration systems are dominant within the European Union member States, but in the Nordic countries, the holding structure is based on a direct holding model. Share registers of all listed companies are public, so basically, anyone may have a full outlook on the corporate ownership structure whenever they want. However, even the Nordic corporate governance model allows you to hide your ownership from public scrutiny by using other companies, most preferably unlisted ones, as intermediaries. The more complicated the chain of intermediaries, the more difficult it is to understand the ownership pattern and level of ownership concentration, which is especially important when evaluating the diversity and functioning of the news media markets. This is why maximum transparency requires not only public share registers but also a structural database, which has also been designed to show the full chain of direct and indirect ownership of each legal owner. This article analyses the relational database model developed for studying and presenting multilevel direct and indirect ownership structures of European news media in the first phase of the Euromedia Ownership Monitor project funded by the European Union.

In developing NoSQL databases, a major motivation is to achieve better efficient query performance compared with relational databases. The graph database is a NoSQL paradigm where navigation is based on links instead of joining tables. Links can be implemented as pointers, and following a pointer is a constant time operation, whereas joining tables is more complicated and slower, even in the presence of foreign keys. Therefore, link-based navigation has been seen as a more efficient query approach than using join operations on tables. Existing studies strongly support this assumption. However, query complexity has received less attention. For example, in enterprise information systems, queries are usually complex so data need to be collected from several tables or by traversing paths of graph nodes of different types. In the present study, we compared the query performance of a graph-based database system (Neo4j) and relational database systems (MySQL and MariaDB). The effect of different efficiency issues (e.g., indexing and optimization) were included in the comparison in order to investigate the most efficient solutions for different query types. The outcome is that although Neo4j is more efficient for simple queries, MariaDB is essentially more efficient when the complexity of queries increases. The study also highlighted how dramatically the efficiency of relational database has grown during the last decade.

Public websites offer information on a variety of topics and services and are accessed by users with varying skills to browse the kind of electronic document repositories. However, the complex website structure and diversity of web browsing behavior create a challenging task for click prediction. This paper presents the results of a novel reinforcement learning approach to model user browsing patterns in a hierarchically ordered municipal website. We study how accurate predictor the browsing history is, when the target pages are not immediate next pages pointed by hyperlinks, but appear a number of levels down the hierarchy. We compare traditional type of baseline classifiers’ performance against our reinforcement learning-based training algorithm.

What are known as specialized or specialist dictionaries are much more than lists of words and their definitions with occasional comments on things such as synonymy and homonymy.That is to say, a particular specialist term may be associated with many other concepts, including quotations, different senses, etymological categories, semantic categories, superordinate and subordinate terms in the terminological hierarchy, spelling variants, and references to background sources discussing the exact meaning and application of the term. The various concepts,in turn, form networks of mutual links, which makes the structure of the background concepts demanding to model when designing a database structure for this type of dictionary. The Dictionary of medical vocabulary in English, 1375–1550 is a specialized historical dictionary that covers the vast medical lexicon of the centuries examined. It comprises over 12,000 terms, each of them associated with a host of background concepts. Compiling the dictionary took over 15 years. The process started with an analysis of hand-written manuscripts and early printed books from different sources and ended with the electronic dictionary described in the present paper. Over these years, the conceptual structure, database schema, and requirements for essential use cases were iteratively developed. In our paper, we introduce the conceptual structure and database schema modelled for implementing an electronic dictionary that involves different use cases such as term insertion and linking a term to related concepts. The achieved conceptual model, database structure, and use cases provide a general framework for reference-oriented specialized dictionaries, including ones with a historical orientation.

Continuously growing amount of data has inspired seeking more and more efficient database solutions for storing and manipulating data. In big data sets, NoSQL databases have been established as alternatives for traditional SQL databases. The effectiveness of these databases has been widely tested, but the tests focused only on key-value data that is structurally very simple. Many application domains, such as telecommunication, involve more complex data structures. Huge amount of Mobile Network Event (MNE) data is produced by an increasing number of mobile and ubiquitous applications. MNE data is structurally predetermined and typically contains a large number of columns. Applications that handle MNE data are usually insert intensive, as a huge amount of data are generated during rush hours. NoSQL provides high scalability and its column family stores suits MNE data well, but NoSQL does not support ACID features of the traditional relational databases. NewSQL is a new kind of databases, which provide the high scalability of NoSQL while still maintaining ACID guarantees of the traditional DBMS. In the paper, we evaluation NEM data storing and aggregating efficiency of Cassandra and ParStream/Kinetic databases and aim to find out whether the new kind of database technology can clearly bring performance advantages over legacy database technology and offers an alternative to existing solutions. Among the column family stores of NoSQL, Cassandra is especially a good choice for insert intensive applications due to its way to handle data insertions. ParStream is a novel and advanced NewSQL like database and is recently integrated into Cisco Kinetic. The results of the evaluation show that ParStream is much faster than Cassandra when storing and aggregating MNE data and the NewSQL is a very strong alternative to existing database solutions for insert intensive applications.

2017, Copyright is with the authors. Published in the Workshop Proceedings of the EDBT/ICDT 2017 Joint Conference (March 21, 2017, Venice, Italy) on CEUR-WS.org (ISSN 1613-0073).

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.

Two XML query languages were tested for intuitivity, learnability and memorability. The languages differ with relation to the query structures like the use of variables, iterators and reference to attributes. One of the languages, XQuery, is a procedural, expressive and data-oriented query language that is suitable even for programming purposes; the other, XIL, is more declarative, document-oriented query language with a simpler syntax. A query-writing test with the learners of the languages was executed. The study indicates that, in the query writing, the more procedural query language yields a greater number of correct queries. Similarity between the tested languages, and to SQL, is discussed from the point of view of learnability.

Hierarchical part-of relationships/aggregation structures and related queries are essential parts of information systems. However, relational database query languages do not explicitly support hierarchical relationships and queries. A hierarchical query may require a great number of join operations, which increases the effort in query formulation. Therefore, we propose path expressions in formulating hierarchical views over relational data because path expressions are a conventional and compact way to represent hierarchical relationships. We embed path expressions within SQL queries and compile them to standard SQL. This ensures that the path expressions can straightforwardly be implemented on the top of standard relational database systems. The compilation of a path expression is given by an attribute grammar, a conventional formalism to define the semantics of a language.

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.

Monitoring the environmental performance of a product is recognized to be increasingly important. The most common method of measuring the environmental performance is the international standards of Life Cycle Assessment (LCA). Typically, measuring is based on estimations and average values at product category level. In this chapter, the authors present a framework for measuring environmental impact at the item level. Using Traceability Graph, emissions and resources can be monitored from the data management perspective. The model can be mapped to any precision level of physical tracing. At the most precise level, even a single physical object and its components can be analyzed. This, of course, demands that the related objects and their components are identified and mapped to the database. From the opposite perspective, the authors’ model also supports rough level analysis of products and their histories. In terms of the Traceability Cube, multidimensional analysis can be applied for traceability data.

A search process is a ternary relationship between the user, the retrieval system and the user interface. A focused retrieval system aims at retrieving the most relevant parts within a relevant document. In focused retrieval the user interface may show not only the relevant documents but also identify the most relevant parts within a document. In document browsing a user may apply several strategies, related to the order of browsing, tolerance to irrelevance and the amount of required information. A model for result document browsing in focused retrieval is introduced. Further, its applicability is illustrated through a simulated experiment.

Unobtrusive user tracking is needed in order to achieve realistic test settings for IIR. This demand is stressed, when studying the usage of mobile devices in IR. In this study, we present a model and a tool for observing and recording user's browsing within a retrieved web document. We extend an existing open source program UsaProxy with functionalities for tracking browsing behaviour.

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.

The tracing of resources and emissions has been recognised increasingly important in supply chains. The developed traceability graph enables tracing of information associated with products and their parts. Tracing in a supply chain requires the three integrated levels of actions: 1 at the physical level the Radio-Frequency Identification (RFID) is used for physical objects; 2 at the data-storing level logical counterparts of RFID tags and database objects are mapped; 3 operational actions determine how recourses, emissions etc. are portioned in different parts of the traceability graph. Briefly, we integrate RFID technology with databases and operational actions of the supply chain.

In enterprise information systems EISs it is necessary to model, integrate and compute very diverse data. In advanced EISs the stored data often are based both on structured e.g. relational and semi-structured e.g. XML data models. In addition, the ad hoc information needs of end-users may require the manipulation of data-oriented structural, behavioural and deductive aspects of data. Contemporary languages capable of treating this kind of diversity suit only persons with good programming skills. In this paper we present a concept-oriented query language approach to manipulate this diversity so that the programming skill requirements are considerably reduced. In our query language, the features which need technical knowledge are hidden in application-specific concepts and structures. Therefore, users need not be aware of the underlying technology. Application-specific concepts and structures are represented by the modelling primitives of the extended RDOOM relational deductive object-oriented modelling which contains primitives for all crucial real world relationships is-a relationship, part-of relationship, association, XML documents and views. Our query language also supports intensional and extensional–intensional queries, in addition to conventional extensional queries. In its query formulation, the end-user combines available application-specific concepts and structures through shared variables.

In a hierarchical XML structure, surrounding elements form the context of an XML element. In document-oriented XML, the context is a part of the semantics of the element and augments its textual information. The process of taking the context of the element into account in element scoring is called contextualization. This study extends the concept of contextualization and presents a classification of contextualization models. In an XML collection, elements are of different granularity, i.e. lower level elements are shorter and carry less textual information. Thus, it seems credible that contextualization interacts differently with diverse elements. Even if it is known that contextualization leads to improved effectiveness in element retrieval, the improvement on different granularity levels has not been investigated. This study explores the effect of contextualization on these levels. Further, a parameterized framework for testing contextualization is presented. The empirical part of the study is carried out in a traditional laboratory setting, where an XML collection is granulated. This is necessary in order to measure performance separately at different hierarchy levels. The results confirm the effectiveness of contextualization, and show how the elements of different granularities benefit from contextualization.

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.