000 Allgemeines, Informatik, Informationswissenschaft
Refine
Document Type
- Journal article (1)
- Book chapter (1)
- Conference proceeding (1)
Has full text
- yes (3) (remove)
Is part of the Bibliography
- yes (3)
Institute
- Informatik (2)
Publisher
- De Gruyter (1)
- IARIA (1)
The Eighth International Conference on Advances in Databases, Knowledge, and Data Applications (DBKDA 2016), held between June 26 - 30, 2016 - Lisbon, Portugal, continued a series of international events covering a large spectrum of topics related to advances in fundamentals on databases, evolution of relation between databases and other domains, data base technologies and content processing, as well as specifics in applications domains databases. Advances in different technologies and domains related to databases triggered substantial improvements for content processing, information indexing, and data, process and knowledge mining. The push came from Web services, artificial intelligence, and agent technologies, as well as from the generalization of the XML adoption. High-speed communications and computations, large storage capacities, and load-balancing for distributed databases access allow new approaches for content processing with incomplete patterns, advanced ranking algorithms and advanced indexing methods. Evolution on e-business, ehealth and telemedicine, bioinformatics, finance and marketing, geographical positioning systems put pressure on database communities to push the ‘de facto’ methods to support new requirements in terms of scalability, privacy, performance, indexing, and heterogeneity of both content and technology.
Bei weitgehend gleicher Ausstattung und neuen Anforderungen an die Hochschulrechenzentren rücken Kooperationen zunehmend in den Mittelpunkt. Für diese,
auch hochschulartenübergreifende, Kooperationen genügt der klassische informelle Rahmen vielfach nicht mehr. Für eine erfolgreiche Zusammenarbeit sind einige Voraussetzungen zu erfüllen. Rechenzentren treten in neuer Rolle als Provider von Dienstleistungen für Nutzer auch außerhalb ihrer eigenen Hochschule auf. Ebenso
könnten sie sich zukünftig verstärkt in der Nutzerperspektive wiederfinden. IT-Service-Einrichtungen müssen sich ihrer neuen Rolle als Diensteanbieter und Nutzer von Diensten Dritter bewusst werden und diese in ihre Überlegungen für die Ausgestaltung neuer Dienste einfließen lassen.
The recent years and especially the Internet have changed the ways in which data is stored. It is now common to store data in the form of transactions, together with ist creation time-stamp. These transactions can often be attributed to Logical units, e.g., all transactions that belong to one customer. These groups, we refer to them as data sequences, have a more complex structure than tuple-based data. This makes it more difficult to find discriminatory patterns for classification purposes. However, the complex structure potentially enables us to track behaviour and its change over the course of time. This is quite interesting, especially in the e-commerce area, in which classification of a sequence of customer actions is still a challenging task for data miners. However, before standard algorithms such as Decision Trees, Neural Nets, Naive Bayes or Bayesian Belief Networks can be applied on sequential data, preparations are required in order to capture the information stored within the sequences. Therefore, this work presents a systematic approach on how to reveal sequence patterns among data and how to construct powerful features out of the primitive sequence attributes. This is achieved by sequence aggregation and the incorporation of time dimension into the feature construction step. The proposed algorithm is described in detail and applied on a real-life data set, which demonstrates the ability of the proposed algorithm to boost the classification performance of well-known data mining algorithms for binary classification tasks.