By Witold Abramowicz
This e-book includes the refereed court cases of the 18th foreign convention on company details platforms, BIS 2015, held in Poznań, Poland, in June 2015. The BIS convention sequence follows traits in educational and company examine; hence, the subject of the BIS 2015 convention was once “Making large information Smarter.” giant facts is now a pretty mature suggestion, famous and popular through execs in either examine and undefined. jointly, they paintings on constructing extra enough and effective instruments for facts processing and interpreting, hence turning "big facts" into "smart data."
The 26 revised complete papers have been rigorously reviewed and chosen from 70 submissions. additionally, invited papers are integrated during this publication. they're grouped into sections on substantial and shrewdpermanent information, semantic applied sciences, content material retrieval and filtering, company procedure administration and mining, collaboration, company structure and business−IT alignment, particular BIS purposes, and open info for BIS.
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Extra resources for Business Information Systems: 18th International Conference, BIS 2015, Poznań, Poland, June 24-26, 2015, Proceedings
We have already summarised traditional approaches for the process of parameter estimation and equation formulation (PEEF) and concluded that these traditional concepts are cumbersome, resource-intensive and are in general only manually applicable . In this paper, we will demonstrate the automated equation formulation and annotation of a given CLD based on a given historical data set. This is done by training neural networks in combination with evolutionary algorithms. The trained neural network is then annotated to the associated CLD element as a function surrogate (FS) that can replay the historical data or predict its future development.
Therefore we create hypothesis 6. 0. For discovering the special attributes, we use a Likert  scale of one to six (1: low to 6: high) for all items (see Table 2). g. ) are ranged on a scale of one to two (1: use, 0: not in use). 2 Research Methods and Data Collection Our quantitative research study based on a web survey in German speaking countries (Germany, Austria and Switzerland). The study were implemented via the open source tool Limesurvey . To ensure a high quality, we tested our model and study with a pre-test and improve our study on the basis of our results.
One reason for this performance gap could be the optimization of execution plans with database technologies in Apache Flink. Further investigations revealed that in Flink pipelining data to the next operator is more eﬃcient than in Spark (see Fig. 4). Another reason for the better Evaluating New Approaches of Big Data Analytics Frameworks 35 Fig. 3. Duration of applications with increasing number of iterations Fig. 4. Join processing in Apache Spark (left) and Flink (right) performance is a quicker join processing of large datasets in Flink, as already seen in point two.