Recap Sitzung 2 – Aufbau einer wissenschaftlichen Arbeit

Unser heutiger Schreibprompt war „Deine Lieblingsszene aus einem Film“. Danke an die Vorleser 🙂

Im Anschluss habe ich das Content-Delivery der heutigen Sitzung an einen Experten delegiert und wir haben uns das Video „How to write a great research paper“  von Simon Peyton Jones angeschaut.

Anschließend haben  wir über das Video geredet und im speziellen über die Struktur einer wissenschaftlichen Arbeit gesprochen (Folien).

Zum Schluss gab es noch eine kurze Übung. Ich habe Euch Abstracts aus Schnipsel zusammenpuzzeln lassen. War gar nicht so einfach, den roten Faden zu finden oder?


Hausaufgaben

(einzureichen bis Donnerstag, 21.04.2016 09:00 Uhr in Euren Blogs)

Aufgabe 1:
Finde zu folgenden zwei Abstracts einen möglichst passenden Titel!

  1. Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although a number of relevant algorithms have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility itemsets. In this paper, we propose two algorithms, namely utility pattern growth (UP-Growth) and UP-Growth+, for mining high utility itemsets with a set of effective strategies for pruning candidate itemsets. The information of high utility itemsets is maintained in a tree-based data structure named utility pattern tree (UP-Tree) such that candidate itemsets can be generated efficiently with only two scans of database. The performance of UP-Growth and UP-Growth+ is compared with the state-of-the-art algorithms on many types of both real and synthetic data sets. Experimental results show that the proposed algorithms, especially UP-Growth+, not only reduce the number of candidates effectively but also outperform other algorithms substantially in terms of runtime, especially when databases contain lots of long transactions.
  2. The essence and value of Linked Data lies in the ability of humans and machines to query, access and reason upon highly structured and formalised data. Ontology structures provide an unambiguous description of the structure and content of data. While a multitude of software applications and visualization systems have been developed over the past years for Linked Data, there is still a significant gap that exists between applications that consume Linked Data and interfaces that have been designed with significant focus on aesthetics. Though the importance of aesthetics in affecting the usability, effectiveness and acceptability of user interfaces have long been recognised, little or no explicit attention has been paid to the aesthetics of Linked Data applications. In this paper, we introduce a formalised approach to developing aesthetically pleasing semantic web interfaces by following aesthetic principles and guidelines identified from literature. We apply such principles to design and develop a generic approach of using visualizations to support exploration of Linked Data, in an interface that is pleasing to users. This provides users with means to browse ontology structures, enriched with statistics of the underlying data, facilitating exploratory activities and enabling visual query for highly precise information needs. We evaluated our approach in three ways: an initial objective evaluation comparing our approach with other well-known interfaces for the semantic web and two user evaluations with semantic web researchers.

Aufgabe 2:
Lest das Kapitel 2 aus Justin Zobels „Writing for Computer Science“ zur Vorbereitung auf die nächste Sitzung.

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