With the growth in social media, online forums have become major sources of data for social network analysis and a major research area in data analytics. However, many aspects of social networks have not been addressed fully, if at all. First, there is the need to capture various parts of the content of the exchanges, for example, bare text and hashtags, which constitute some of the major features of social media networks. Second, as in all social networks, there are many different types of relationships; for example, “following” and “friend” relationships, as well as relationships defined in terms of how users interact with one another such as replying, quoting, retweeting, and mentioning. Finally, an important aspect of analysis of social networks is temporal dynamics and topic evolution. We offer an analysis of Twitter data using tensors which can incorporate all of these different aspects in a single representation and both identify salient events and distinguish important views of these events.

A large portion of real world data is either text or structured (e.g., relational) data. Moreover, such data objects are often linked together (e.g., structured specification of products linking with the corresponding product descriptions and customer comments). Even for text data such as news data, typed entities can be extracted with entity extraction tools. The EventCube project constructs TextCube and TopicCube from interconnected structured and text data (or from text data via entity extraction and dimension building), and performs multidimensional search and analysis on such datasets, in an informative, powerful, and user-friendly manner. This proposed EventCube demo will show the power of the system not only on the originally designed ASRS (Aviation Safety Report System) data sets, but also on news datasets collected from multiple news agencies, and academic datasets constructed from the DBLP and web data. The system has high potential to be extended in many powerful ways and serve as a general platform for search, OLAP (online analytical processing) and data mining on integrated text and structured data. After the system demo in the conference, the system will be put on the web for public access and evaluation.

While there are many aspects to managing corporate knowledge, one key issue is how to organize corporate documents into categories of interest. In this paper, we focus on using user assisted text classification in conjunction with a web portal, multiple document management systems and an ontology, to provide a powerful solution for organizing information about a company's technology. We propose a system that interacts with an author using an automatic text classifier to suggest controlled keywords to be used as metadata. The proposed approach does not require professional librarians or that the end users have extensive training. The use of a controlled vocabulary allows for a more consistent description of corporate documents, and promotes easier access by people across the company. It is easier to find similar documents which use different nomenclature. Finally, the interactive nature of the system results in a more correct and precise description of each document than a fully automatic system would.