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Online Analytical Processing, or OLAP, is an approach to answering multidimensional analytical (MDA) queries in an interactive way. However, the traditional OLAP approaches can only deal with structured data, but not unstructured textual data like tweets. To address this problem, we propose a Latent Dirichlet Allocation (LDA)-based model, called Multilayered Semantic LDA (MS-LDA), which detects the hidden layered interests from Twitter data based on LDA. The layered dimension of interests can be further used to apply OLAP techniques to Twitter data. Furthermore, MS-LDA employs the semantic similarity among words of tweets based on word2vec, and also the social relationship among twitters, to improve its effectiveness. The extensive experiments demonstrate that MS-LDA can effectively extract the dimension hierarchy of tweeters' interests for OLAP.

Innovation processes are out of necessity and by definition always open-ended and embedded in social interests and beliefs. This paper reports a study of how a power-split technology for hybrid vehicles needed and found support and interest from a variety of social actors to reach the market. One of the principal challenges of innovators is to mobilise support and attract interest among relevant social groups; innovations are always, to some extent, dependent on their ability to conform to pre-existing practices and beliefs. Based on a study of a hybrid electric vehicle (HEV) project at Volvo Cars, this paper investigates how the concept of interessement can contribute to a better understanding of the innovation processes (Akrich et al., 2002a,b). It concludes that this concept contributes to interesting perspectives on the dual technological and social nature of the innovation process but that the idea of one central innovator driving the process all the way may not always be applicable.