Fast PageRank Computation via a Sparse Linear System (Extended Abstract)

Gianna M. Del Corso Antonio Gull 1,2# Francesco Romani Dipartimento di Informatica, University of Pisa, Italy 2 IIT-CNR, Pisa Keywords: Link Analysis, Search Engines, Web Matrix Reducibility and Permutation.

One-Sided Communication for High Performance Computing Applications

Thesis (Ph.D.) - Indiana University, Computer Sciences, 2009Parallel programming presents a number of critical challenges to application developers. Traditionally, message passing, in which a process explicitly sends data and another explicitly receives the data, has been used to program parallel applications. With the recent growth in multi-core processors, the level of parallelism necessary for next generation machines is cause for concern in the message passing community. The one-sided programming paradigm, in which only one of the two processes involved in communication actively participates in message transfer, has seen increased interest as a potential replacement for message passing. One-sided communication does not carry the heavy per-message overhead associated with modern message passing libraries. The paradigm offers lower synchronization costs and advanced data manipulation techniques such as remote atomic arithmetic and synchronization operations. These combine to present an appealing interface for applications with random communication patterns, which traditionally present message passing implementations with difficulties. This thesis presents a taxonomy of both the one-sided paradigm and of applications which are ideal for the one-sided interface. Three case studies, based on real-world applications, are used to motivate both taxonomies and verify the applicability of the MPI one-sided communication and Cray SHMEM one-sided interfaces to real-world problems. While our results show a number of short-comings with existing implementations, they also suggest that a number of applications could benefit from the one-sided paradigm. Finally, an implementation of the MPI one-sided interface within Open MPI is presented, which provides a number of unique performance features necessary for efficient use of the one-sided programming paradigm

Mining Web Dynamics for Search

Billions of web users collectively contribute to a dynamic web that preserves how information sources and descriptions change over time. This dynamic process sheds light on the quality of web content, and even indicates the temporal properties of information needs expressed via queries. However, existing commercial search engines typically utilize one crawl of web content (the latest) without considering the complementary information concealed in web dynamics. As a result, the generated rankings may be biased due to the efficiency of knowledge on page or hyperlink evolution, and the time-sensitive facet within search quality, e.g., freshness, has to be neglected. While previous research efforts have been focused on exploring the temporal dimension in retrieval process, few of them showed consistent improvements on large-scale real-world archival web corpus with a broad time span.We investigate how to utilize the changes of web pages and hyperlinks to improve search quality, in terms of freshness and relevance of search results. Three applications that I have focused on are: (1) document representation, in which the anchortext (short descriptive text associated with hyperlinks) importance is estimated by considering its historical status; (2) web authority estimation, in which web freshness is quantified and utilized for controlling the authority propagation; and (3) learning to rank, in which freshness and relevance are optimized simultaneously in an adaptive way depending on query type. The contributions of this thesis are: (1) incorporate web dynamics information into critical components within search infrastructure in a principled way; and (2) empirically verify the proposed methods by conducting experiments based on (or depending on) a large-scale real-world archival web corpus, and demonstrated their superiority over existing state-of-the-art

On Two Web IR Boosting Tools: Clustering and Ranking

This thesis investigates several research problems which arise in modern Web Information Retrieval (WebIR). The Holy Grail of modern WebIR is to find a way to organize and to rank results so that the most ``relevant' come first. The first break-through technique was the exploitation of the link structure of the Web graph in order to rank the result pages, using the well-known Hits and Pagerank algorithms. This link-analysis approaches have been improved and extended, but yet they seem to be insufficient in providing a satisfying search experience. In a number of situations a flat list of search results is not enough, and the users might desire to have search results grouped on-the-fly in folders of similar topics. In addition, the folders should be annotated with meaningful labels for rapid identification of the desired group of results. In other situations, users may have different search goals even when they express them with the same query. In this case the search results should be personalized according to the users' on-line activities. In order to address this need, we will discuss the algorithmic ideas behind SnakeT, a hierarchical clustering meta-search engine which personalizes searches according to the clusters selected by users on-the-fly. There are also situations where users might desire to access fresh information. In these cases, traditional link analysis could not be suitable. In fact, it is possible that there is not enough time to have many links pointing to a recently produced piece of information. In order to address this need, we will discuss the algorithmic and numerical ideas behind a new ranking algorithm suitable for ranking fresh type of information, such as news articles or blogs. When link analysis suffices to produce good quality search results, the huge amount of Web information asks for fast ranking methodologies. We will discuss numerical methodologies for accelerating the eingenvector-like computation, commonly used by link analysis. An important result of this thesis is that we show how to address the above predominant issues of Web Information Retrieval by using clustering and ranking methodologies. We will demonstrate that both clustering and ranking have a mutual reinforcement propriety which has not yet been studied intensively. This propriety can be exploited to boost the precision of both the two methodologies