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.

We propose a novel system TEXplorer that integrates keyword-based object ranking with the aggregation and exploration power of OLAP in a text database with rich structured attributes available, e.g., a product review database. TEXplorer can be implemented within a multi-dimensional text database, where each row is associated with structural dimensions (attributes) and text data (e.g., a document). The system utilizes the text cube data model, where a cell aggregates a set of documents with matching values in a subset of dimensions. Cells in a text cube capture different levels of summarization of the documents, and can represent objects at different conceptual levels.

The prevalence of Web 2.0 techniques has led to the boom of various online communities, where topics spread ubiquitously among user-generated documents. Working together with this diffusion process is the evolution of topic content, where novel contents are introduced by documents which adopt the topic. Unlike explicit user behavior (e.g., buying a DVD), both the diffusion paths and the evolutionary process of a topic are implicit, making their discovery challenging. In this paper, we track the evolution of an arbitrary topic and reveal the latent diffusion paths of that topic in a social community. A novel and principled probabilistic model is proposed which casts our task as an joint inference problem, which considers textual documents, social influences, and topic evolution in a unified way. Specifically, a mixture model is introduced to model the generation of text according to the diffusion and the evolution of the topic, while the whole diffusion process is regularized with user-level social influences through a Gaussian Markov Random Field. Experiments on both synthetic data and real world data show that the discovery of topic diffusion and evolution benefits from this joint inference, and the probabilistic model we propose performs significantly better than existing methods.

With the development of Web applications, textual documents are not only getting richer, but also ubiquitously interconnected with users and other objects in various ways, which brings about text-rich heterogeneous information networks. Topic models have been proposed and shown to be useful for document analysis, and the interactions among multi-typed objects play a key role at disclosing the rich semantics of the network. However, most of topic models only consider the textual information while ignore the network structures or can merely integrate with homogeneous networks. None of them can handle heterogeneous information network well. In this paper, we propose a novel topic model with biased propagation (TMBP) algorithm to directly incorporate heterogeneous information network with topic modeling in a unified way. The underlying intuition is that multi-typed objects should be treated differently along with their inherent textual information and the rich semantics of the heterogeneous information network. A simple and unbiased topic propagation across such a heterogeneous network does not make much sense. Consequently, we investigate and develop two biased propagation frameworks, the biased random walk framework and the biased regularization framework, for the TMBP algorithm from different perspectives, which can discover latent topics and identify clusters of multi-typed objects simultaneously. We extensively evaluate the proposed approach and compare to the state-of-the-art techniques on several datasets. Experimental results demonstrate that the improvement in our proposed approach is consistent and promising.

Previous studies on supporting free-form keyword queries over RDBMSs provide users with linked structures (e.g., a set of joined tuples) that are relevant to a given keyword query. Most of them focus on ranking individual tuples from one table or joins of multiple tables containing a set of keywords. In this paper, we study the problem of keyword search in a data cube with text-rich dimension(s) (so-called text cube). The text cube is built on a multidimensional text database, where each row is associated with some text data (a document) and other structural dimensions (attributes). A cell in the text cube aggregates a set of documents with matching attribute values in a subset of dimensions. We define a keyword-based query language and an IR-style relevance model for scoring/ranking cells in the text cube. Given a keyword query, our goal is to find the top-k most relevant cells. We propose four approaches: inverted-index one-scan, document sorted-scan, bottom-up dynamic programming, and search-space ordering. The search-space ordering algorithm explores only a small portion of the text cube for finding the top-k answers, and enables early termination. Extensive experimental studies are conducted to verify the effectiveness and efficiency of the proposed approaches.

On-Line Analytical Processing (OLAP) has shown great success in many industry applications, including sales, marketing, management, financial data analysis, etc. In this paper, we propose Visual Cube and multi-dimensional OLAP of image collections, such as web images indexed in search engines (e.g., Google and Bing), product images (e.g. Amazon) and photos shared on social networks (e.g., Facebook and Flickr). It provides online responses to user requests with summarized statistics of image information and handles rich semantics related to image visual features. A clustering structure measure is proposed to help users freely navigate and explore images. Efficient algorithms are developed to construct Visual Cube. In addition, we introduce the new issue of Cell Overlapping in data cube and present efficient solutions for Visual Cube computation and OLAP operations. Extensive experiments are conducted and the results show good performance of our algorithms.

Previous studies on supporting keyword queries in RDBMSs provide users with a ranked list of relevant linked structures (e.g. joined tuples) or individual tuples. In this paper, we aim to support keyword search in a data cube with text-rich dimension(s) (so-called text cube). Each document is associated with structural dimensions. A cell in the text cube aggregates a set of documents with matching dimension values on a subset of dimensions. Given a keyword query, our goal is to find the top-k most relevant cells in the text cube. We propose a relevance scoring model and efficient ranking algorithms. Experiments are conducted to verify their efficiency.

User generated information in online communities has been characterized with the mixture of a text stream and a network structure both changing over time. A good example is a web-blogging community with the daily blog posts and a social network of bloggers.

The World-Wide Web consists not only of a huge number of unstructured texts, but also a vast amount of valuable structured data. Web tables [2] are a typical type of structured information that are pervasive on the web, and Web-scale methods that automatically extract web tables have been studied extensively [1]. Many powerful systems (e.g.OCTOPUS [4], Mesa [3]) use extracted web tables as a fundamental component.

Graphs are prevalent in many domains such as Bioinformatics, social networks, Web and cyber-security. Graph pattern mining has become an important tool in the management and analysis of complexly structured data, where example applications include indexing, clustering and classification. Existing graph mining algorithms have achieved great success by exploiting various properties in the pattern space . Unfortunately, due to the fundamental role subgraph isomorphism plays in these methods, they may all enter into a pitfall when the cost to enumerate a huge set of isomorphic embeddings blows up, especially in large graphs. The solution we propose for this problem resorts to reduction on the data space . For each graph, we build a summary of it and mine this shrunk graph instead. Compared to other data reduction techniques that either reduce the number of transactions or compress between transactions, this new framework, called Summarize-Mine, suggests a third path by compressing within transactions . Summarize-Mine is effective in cutting down the size of graphs, thus decreasing the embedding enumeration cost. However, compression might lose patterns at the same time. We address this issue by generating randomized summaries and repeating the process for multiple rounds, where the main idea is that true patterns are unlikely to miss from all rounds. We provide strict probabilistic guarantees on pattern loss likelihood. Experiments on real malware trace data show that Summarize-Mine is very efficient, which can find interesting malware fingerprints that were not revealed previously.

Nowadays, most business, administration, and/or scientific databases contain both structured attributes and text attributes. We call a database that consists of both multidimensional structured data and narrative text data as multidimensional text database . Searching, OLAP, and mining such databases pose many research challenges. To enhance the power of data analysis, interesting entities and relationships can be extracted from such databases to derive heterogeneous information networks, which in turn will substantially increase the power and flexibility of data exploration in such databases. Based on our previous studies on TextCube [1], TopicCube [2], and information network analysis, such as RankClus [3] and NetClus [4], we construct iNextCube , an i nformation- N etwork- e nhanced te xt Cube . In this demo, we show the power of iNextCube in the search and analysis of two multidimensional text databases: (i) a DBLP-based CS bibliographic database, and (ii) an online news database.

In the past, quite a few fast algorithms have been developed to mine frequent patterns over graph data, with the large spectrum covering many variants of the problem. However, the real bottleneck for knowledge discovery on graphs is neither efficiency nor scalability, but the usability of patterns that are mined out. Currently, what the state-of-art techniques give is a lengthy list of exact patterns, which are undesirable in the following two aspects: (1) on the micro side, due to various inherent noises or data diversity, exact patterns are usually not too useful in many real applications; and (2) on the macro side, the rigid structural requirement being posed often generates an excessive amount of patterns that are only slightly different from each other, which easily overwhelm the users.

Since Jim Gray introduced the concept of rdquodata cuberdquo in 1997, data cube, associated with online analytical processing (OLAP), has become a driving engine in data warehouse industry. Because the boom of Internet has given rise to an ever increasing amount of text data associated with other multidimensional information, it is natural to propose a data cube model that integrates the power of traditional OLAP and IR techniques for text. In this paper, we propose a text-cube model on multidimensional text database and study effective OLAP over such data. Two kinds of hierarchies are distinguishable inside: dimensional hierarchy and term hierarchy. By incorporating these hierarchies, we conduct systematic studies on efficient text-cube implementation, OLAP execution and query processing. Our performance study shows the high promise of our methods.