Exploratory analytics on patent data sets using the SIMPLE platform

Exploratory Analytics is the process of analyzing data for the purpose of forming hypotheses. Patent data sets, because they are relatively large and diverse and because they consist of a mixture of structured and unstructured information present a formidable challenge and a great opportunity in applying exploratory analytics techniques. In this paper we describe methods we have implemented for effective exploratory analytics on patent data sets using an interactive approach and a web based software tool called SIMPLE. We use real-world case studies to demonstrate the effectiveness of our exploratory analytics approach in the discovery of useful information from the patent corpus.     

Statistical sequential analysis for real-time video scene change detection on compressed multimedia bitstream

The increased availability and usage of multimedia information have created a critical need for efficient multimedia processing algorithms. These algorithms must offer capabilities related to browsing, indexing, and retrieval of relevant data. A crucial step in multimedia processing is that of reliable video segmentation into visually coherent video shots through scene change detection. Video segmentation enables subsequent processing operations on video shots, such as video indexing, semantic representation, or tracking of selected video information. Since video sequences generally contain both abrupt and gradual scene changes, video segmentation algorithms must be able to detect a large variety of changes. While existing algorithms perform relatively well for detecting abrupt transitions (video cuts), reliable detection of gradual changes is much more difficult. A novel one-pass, real-time approach to video scene change detection based on statistical sequential analysis and operating on a compressed multimedia bitstream is proposed. Our approach models video sequences as stochastic processes, with scene changes being reflected by changes in the characteristics (parameters) of the process. Statistical sequential analysis is used to provide an unified framework for the detection of both abrupt and gradual scene changes.     

Model T: A model for user registration patterns based on campus WLAN data

We discuss the derivation of an empirical model for spatial registration patterns of mobile users in a campus wireless local area network (WLAN). Such a model can be very useful in a variety of simulation studies of the performance of mobile wireless systems, such as that of resource management and mobility management protocols. We base the model on extensive experimental data from a campus WiFi LAN installation. We divide the empirical data available to us into training and test data sets, develop the model based on the training set, and evaluate it against the test set. The model shows that user registration patterns exhibit a distinct hierarchy, and that WLAN access points (APs) can be clustered based on registration patterns. Cluster size distributions are highly skewed, as are intra-cluster transition probabilities and trace lengths, which can all be modeled well by the heavy-tailed Weibull distribution. The fraction of popular APs in a cluster, as a function of cluster size, can be modeled by exponential distributions. There is general similarity across hierarchies, in that inter-cluster registration patterns tend to have the same characteristics and distributions as intra-cluster patterns. In this context, we also introduce and discuss the modeling of the disconnected state as an integral part of real traffic characteristics. We generate synthetic traffic traces based on the model we derive. We then compare these traces against the real traces from the test set using a set of metrics we define. We find that the synthetic traces agree very well with the test set in terms of the metrics. We compare the derived model to a simple modified random waypoint model, and show that the latter is not at all representative of the real data. We also show how the model parameters can be varied to allow designers to consider ‘what-if’ scenarios easily. Finally we develop an extended version of Model T that uses an alternative modeling of relative popularity of APs and clusters, with certain generalization advantages, and evaluate its fidelity to the real data also, with positive results. Ravi Jain is currently with Google, Mountain View, California, and until recently was Vice President and Director of the Network Services and Security Lab in DoCoMo USA Labs. At DoCoMo he led the US team designing DoCoMo’s 4th generation core network in three key areas: mobility management, QoS and security. He also led the development of techniques for location estimation, data mining for location prediction, as well as authenticated media delivery and WiFi VoIP. From 1992 to 2002, he worked for ten years on mobile wireless architectures, algorithms, protocols and middleware, as well as open programmable networks, in Applied Research, Telcordia Technologies (formerly Bellcore). Prior to that he worked for several years on systems and communications software development, performance modeling, and parallel programming. Ravi Jain received the Ph.D. in Computer Science from the University of Texas at Austin in 1992. Ravi has numerous publications and patents in the wireless area. He is an Area Editor for IEEE Transactions on Mobile Computing and ACM WINET, and has been Guest Editor for Special Issues of several journals. He was the Program Co-Chair for the 2004 ACM MobiCom conference. He has served on the advisory boards of startup companies in the wireless area. Ravi was also the Specification Lead for the JAIN industry forum specification on Java Call Control (JCC) for Next Generation Networks, as well as the Parlay industry forum Parlay X working group; he is co-author of the book Programming Converged Networks (John Wiley, 2005). Ravi is a member of the Upsilon Pi Epsilon and Phi Kappa Phi honorary societies, a senior member of IEEE, and a member of the ACM. Dan Lelescu has been a researcher with the Media Lab of NTT DoCoMo Communications Labs, San Jose, California since 2002. Prior to that he was a research engineer and later director of the Algorithms group with Compression Science, a start-up video coding company in Silicon Valley. His areas of interest include image and video signal processing, statistical signal processing, computer vision, and wireless communications and networking. He received the Ph.D. in Electrical Engineering and Computer Science from the University of Illinois, Chicago in 2001. He has published in the areas of visual signal processing, content-based image and video retrieval, video coding, and wireless communications, and has six pending patents. He has also made contributions to MPEG video standards. He was a Session chair for the IEEE Communications and Networking (CCNC2006) conference, and is a Guest Editor for EURASIP Journal on Wireless Communications and Networking (2007) and for the Journal of Advances in Multimedia (2007). He is a member of IEEE. Mahadevan Balakrishnan obtained his MS in Electrical engineering from Illinois Institute of Technology, Chicago. As a Research Engineer with DoCoMo Labs, Mahadevan was involved in developing a user mobility model from Wireless LAN user mobility traces obtained from real scenarios. His background includes signal processing, communication theory and networking.