42,878 research outputs found
The contribution of data mining to information science
The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research
Inferring Concise Specifications of APIs
Modern software relies on libraries and uses them via application programming
interfaces (APIs). Correct API usage as well as many software engineering tasks
are enabled when APIs have formal specifications. In this work, we analyze the
implementation of each method in an API to infer a formal postcondition.
Conventional wisdom is that, if one has preconditions, then one can use the
strongest postcondition predicate transformer (SP) to infer postconditions.
However, SP yields postconditions that are exponentially large, which makes
them difficult to use, either by humans or by tools. Our key idea is an
algorithm that converts such exponentially large specifications into a form
that is more concise and thus more usable. This is done by leveraging the
structure of the specifications that result from the use of SP. We applied our
technique to infer postconditions for over 2,300 methods in seven popular Java
libraries. Our technique was able to infer specifications for 75.7% of these
methods, each of which was verified using an Extended Static Checker. We also
found that 84.6% of resulting specifications were less than 1/4 page (20 lines)
in length. Our technique was able to reduce the length of SMT proofs needed for
verifying implementations by 76.7% and reduced prover execution time by 26.7%
Graph-based discovery of ontology change patterns
Ontologies can support a variety of purposes, ranging from capturing conceptual knowledge to the organisation of digital content and information. However, information systems are always subject to change and ontology change management can pose challenges. We investigate ontology change representation and discovery of change patterns.
Ontology changes are formalised as graph-based change logs. We use attributed graphs, which are typed over a generic graph with node and edge attribution.We analyse ontology change logs, represented as graphs, and identify frequent change sequences. Such sequences are applied as a reference in order to discover reusable, often domain-specific and usagedriven change patterns. We describe the pattern discovery algorithms and measure their performance using experimental result
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