2844508
Visualizing tags over time
We consider the problem of visualizing the evolution of tags within the Flickr (flickr.com) online image sharing community. Any user of the Flickr service may append a tag to any photo in the system. Over the past year, users have on average added over a million tags each week. Understanding the evolution of these tags over time is therefore a challenging task. We present a new approach based on a characterization of the most interesting tags associated with a sliding interval of time. An animation provided via Flash in a web browser allows the user to observe and interact with the interesting tags as they evolve over time.New algorithms and data structures are required to support the efficient generation of this visualization. We combine a novel solution to an interval covering problem with extensions to previous work on score aggregation in order to create an efficient backend system capable of producing visualizations at arbitrary scales on this large dataset in real time.
53245339
Randomized Algorithms
For many applications, a randomized algorithm is either the simplest or the fastest algorithm available, and sometimes both. This book introduces the basic concepts in the design and analysis of randomized algorithms. The first part of the text presents basic tools such as probability theory and probabilistic analysis that are frequently used in algorithmic applications. Algorithmic examples are also given to illustrate the use of each tool in a concrete setting. In the second part of the book, each chapter focuses on an important area to which randomized algorithms can be applied, providing a comprehensive and representative selection of the algorithms that might be used in each of these areas. Although written primarily as a text for advanced undergraduates and graduate students, this book should also prove invaluable as a reference for professionals and researchers.
8030181
Finding near neighbors through cluster pruning
Finding near(est) neighbors is a classic, difficult problem in data management and retrieval, with applications in text and image search,in finding similar objects and matching patterns. Here we study cluster pruning, an extremely simple randomized technique. During preprocessing we randomly choose a subset of data points to be leaders the remaining data points are partitioned by which leader is the closest. For query processing, we find the leader(s) closest to the query point. We then seek the nearest neighbors for the query point among only the points in the clusters of the closest leader(s). Recursion may be used in both preprocessing and in search. Such schemes seek approximate nearest neighbors that are "almost as good" as the nearest neighbors. How good are these approximations and how much do they save in computation.
 Our contributions are: (1) we quantify metrics that allow us to study the tradeoff between processing and the quality of the approximate nearest neighbors; (2) we give rigorous theoretical analysis of our schemes, under natural generative processes (generalizing Gaussian mixtures) for the data points; (3) experiments on both synthetic data from such generative processes, as well as on from a document corpus, confirming that we save orders of magnitude in query processing cost at modest compromises in the quality of retrieved points. In particular, we show that p-spheres, a state-of-the-art solution, is outperformed by our simple scheme whether the data points are stored in main or in external memo.
11776735
Query incentive networks
The concurrent growth of on-line communities exhibiting large-scale social structure, and of large decentralized peer-to-peer file-sharing systems, has stimulated new interest in understanding networks of interacting agents as economic systems. Here we formulate a model for query incentive networks, motivated by such systems: users seeking information or services can pose queries, together with incentives for answering them, that are propagated along paths in a network. This type of information-seeking process can be formulated as a game among the nodes in the network, and this game has a natural Nash equilibrium. In such systems, it is a fundamental question to understand how much incentive is needed in order for a node to achieve a reasonable probability of obtaining an answer to a query from the network. We study the size of query incentives as a function both of the rarity of the answer and the structure of the underlying network. This leads to natural questions related to strategic behavior in branching processes. Whereas the classically studied criticality of branching processes is centered around the region where the branching parameter is 1, we show in contrast that strategic interaction in incentive propagation exhibits critical behavior when the branching parameter is 2.
15130102
Towards a Grid enabled system for multicomponent materials design
We are developing a portal for multicomponent materials design using Grid-enabled large-scale simulations. In this paper we report on our services based application architecture which allows integration of simulation software with Grid services to provide a Web-based computational laboratory for the modeling of Al-Cu-Mg-Si alloys. We examine user requirements and describe the design of our framework. Our architecture is implemented using existing middleware such as the Globus and Java CoG toolkits. An interesting feature of our design is the separation of the high-level specification of the materials modeling system from the implementation through the use of a markup language such as XML. We use markup languages with domain-specific extensions to specify (in architecture-independent form) rules and constraints that allow meaningful composition of simulation tasks and experimentally determined material properties. In addition, we use them to specify application code interfaces so that our simulation server can be dynamically reconfigured to include new software and constraints.