Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

Constraint-wish and satisfied-dissatisfied: an overview of two approaches for dealing with bipolar querying

In recent years, there has been an increasing interest in dealing with user preferences in flexible database querying, expressing both positive and negative information in a heterogeneous way. This is what is usually referred to as bipolar database querying. Different frameworks have been introduced to deal with such bipolarity. In this chapter, an overview of two approaches is given. The first approach is based on mandatory and desired requirements. Hereby the complement of a mandatory requirement can be considered as a specification of what is not desired at all. So, mandatory requirements indirectly contribute to negative information (expressing what the user does not want to retrieve), whereas desired requirements can be seen as positive information (expressing what the user prefers to retrieve). The second approach is directly based on positive requirements (expressing what the user wants to retrieve), and negative requirements (expressing what the user does not want to retrieve). Both approaches use pairs of satisfaction degrees as the underlying framework but have different semantics, and thus also different operators for criteria evaluation, ranking, aggregation, etc

Information Integration - the process of integration, evolution and versioning

At present, many information sources are available wherever you are. Most of the time, the information needed is spread across several of those information sources. Gathering this information is a tedious and time consuming job. Automating this process would assist the user in its task. Integration of the information sources provides a global information source with all information needed present. All of these information sources also change over time. With each change of the information source, the schema of this source can be changed as well. The data contained in the information source, however, cannot be changed every time, due to the huge amount of data that would have to be converted in order to conform to the most recent schema.\ud In this report we describe the current methods to information integration, evolution and versioning. We distinguish between integration of schemas and integration of the actual data. We also show some key issues when integrating XML data sources

DataHub: Collaborative Data Science & Dataset Version Management at Scale

Relational databases have limited support for data collaboration, where teams collaboratively curate and analyze large datasets. Inspired by software version control systems like git, we propose (a) a dataset version control system, giving users the ability to create, branch, merge, difference and search large, divergent collections of datasets, and (b) a platform, DataHub, that gives users the ability to perform collaborative data analysis building on this version control system. We outline the challenges in providing dataset version control at scale.Comment: 7 page

Combining quantifications for flexible query result ranking

Databases contain data and database systems governing such databases are often intended to allow a user to query these data. On one hand, these data may be subject to imperfections, on the other hand, users may employ imperfect query preference specifications to query such databases. All of these imperfections lead to each query answer being accompanied by a collection of quantifications indicating how well (part of) a group of data complies with (part of) the user's query. A fundamental question is how to present the user with the query answers complying best to his or her query preferences. The work presented in this paper first determines the difficulties to overcome in reaching such presentation. Mainly, a useful presentation needs the ranking of the query answers based on the aforementioned quantifications, but it seems advisable to not combine quantifications with different interpretations. Thus, the work presented in this paper continues to introduce and examine a novel technique to determine a query answer ranking. Finally, a few aspects of this technique, among which its computational efficiency, are discussed