Chao Zhang

Zhao dai cong shu. [561 zhong]

Xin Liu

Song Ci

Pinyi Ren

Pei Liu

This book constitutes the thoroughly refereed post-conference proceedings of the 6th International ICST Conference on Wireless Internet (WICON 2011) held in Xi’an, Shaanxi, China in October 2011. The 55 revised full papers were carefully selected from numerous submissions and focus on research in telecommunication and networking and development in information and communication technologies. The papers are presented in 10 technical sessions and cover topics as video streaming over OFDMA downlink systems, cognitive radio networks, distributed antenna systems, joint source channel coding, multiuser MIMO systems, signal detection, frequency synchronization in 3GPP LTE systems, visual cognitive radio, wireless relay networks, and network flow identifying methods.

6th International ICST Conference, WICON 2011, Xi'an, China, October 19-21, 2011, Revised Selected Papers

Wireless Internet

Jiawei Han

Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional—they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task. This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making. The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.

Multidimensional Mining of Massive Text Data

Chao Zhang

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