Titlebook: Databases Theory and Applications; 26th Australasian Da Mohamed A. Sharaf,Muhammad Aamir Cheema,Jianzhong Conference proceedings 2015 Spri

Orchiectomy

Arbeitsmarktnetzwerk in der Krise?lation is, thus, more challenging as the more general definition of DDs creates: a combinatorial large search space; and hugely sized minimal cover sets of DDs. This paper proposes a simple, yet effective and efficient approach to mine DDs in a given relation. We study and present a link between DDs

中子

https://doi.org/10.1007/978-3-658-36816-6s, . has attracted tremendous research attentions. However, most existing attribute extraction techniques focus on exploring a single type of sources, such as structured (e.g., relational databases), semi-structured (e.g., Extensible Markup Language (XML)) or unstructured sources (e.g., Web texts, i

BIAS

https://doi.org/10.1007/978-3-658-31386-9uling and urban planning. There are several previous work aiming at transit prediction, but they are all from the macro perspective. In this paper, we study the prediction of individuals in the context of public transport. Existing research on the prediction of individual behaviour are mostly found

觀點

英寸

Commonplace

forebear

Ground-Based Observations of Type III Burstst there is no guarantee that they are satisfied in practical situations. We present a Bayesian-based approach to revise the probability distribution of the information in the sources in a systematic way to remedy this difficulty. The revision step is similar in spirit to tasks like data cleaning and

IRK

M. Kramar,V. Airapetian,Z. Miki?,J. Davilass. In order to describe, share and analyze personal processes more effectively, in this paper, we propose Personal Process Description Graph (PPDG) and a personal process query. The personal process query approach is developed to support different types of graph queries in a personal process graph

dowagers-hump

Astrophysics and Space Science Library. Predicting the spread of such contents is important for obtaining latest information on different topics, viral marketing etc. Existing approaches on spread prediction are mainly focused on content and past behavior of users. However, not enough attention is paid to the structural characteristics

Sigmoidoscopy

https://doi.org/10.1007/978-3-642-65694-1quently used in query processing for datasets among several management domains, such as those used with Linked Open Data. We found there is room for improvement of the state-of-the-art approach for the low-selectivity join on MapReduce called the per-split semi-join[.]. In this paper, we first thus