Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labeled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

/pdf/link-prediction-in-complex-networks-a-survey-fcztp3ymq3.pdf

Link prediction in complex networks: A survey

인공고관절의 세라믹 볼 헤드의 기계적 안정성 평가를 위한 3 차원 유한요소 해석

https://people.mpi-inf.mpg.de/~kberberi/publications/2010-mpii-tra.pdf

YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia

https://pure.mpg.de/pubman/item/item_1819068_3/component/file_1840695/MPI-I-2007-5-003.pdf

YAGO: A Large Ontology from Wikipedia and WordNet

The World Wide Web has become a key source of knowledge pertaining to almost every walk of life. Unfortunately, much of data on the Web is highly ephemeral in nature, with more than 50-80% of content estimated to be changing within a short time. Continuing the pioneering efforts of many national (digital) libraries, organizations such as the International Internet Preservation Consortium (IIPC), the Internet Archive (IA) and the European Archive (EA) have been tirelessly working towards preserving the ever changing Web.However, while these web archiving efforts have paid significant attention towards long term preservation of Web data, they have paid little attention to developing an global-scale infrastructure for collecting, archiving, and performing historical analyzes on the collected data. Based on insights from our recent work on building text analytics for Web Archives, we propose EverLast, a scalable distributed framework for next generation Web archival and temporal text analytics over the archive. Our system is built on a loosely-coupled distributed architecture that can be deployed over large-scale peer-to-peer networks. In this way, we allow the integration of many archival efforts taken mainly at a national level by national digital libraries. Key features of EverLast include support of time-based text search & analysis and the use of human-assisted archive gathering. In this paper, we outline the overall architecture of EverLast, and present some promising preliminary results.

/pdf/everlast-a-distributed-architecture-for-preserving-the-web-34o3l9z061.pdf

EverLast: a distributed architecture for preserving the web

Suffix-trees are popular indexing structures for various sequence processing problems in biological data management. We investigate here the possibility of enhancing the search efficiency of disk-resident suffix-trees through customized layouts of tree-nodes to disk-pages. Specifically, we propose a new layout strategy, called Stellar, that provides significantly improved search performance on a representative set of real genomic sequences. Further, Stellar supports both the standard root-to-leaf lookup queries as well as sophisticated sequencesearch algorithms that exploit the suffix-links of suffix-trees. Our results are encouraging with regard to the ultimate objective of seamlessly integrating sequence processing in database engines.

https://dsl.cds.iisc.ac.in/publications/conference/stellar.pdf

Search-Optimized suffix-tree storage for biological applications

Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called "check-in" profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.

Inferring and Exploiting Categories for Next Location Prediction

An increasing number of temporally versioned text collections is available today with Web archives being a prime example. Search on such collections, however, is often not satisfactory and ignores their temporal dimension completely. Time-travel text search solves this problem by evaluating a keyword query on the state of the text collection as of a user-specified time point. This work demonstrates our approach to efficient time-travel text search and its implementation in the FLUXCAPACITOR prototype.

FluxCapacitor: Efficient Time-Travel Text Search

Modern text analytics applications operate on large volumes of temporal text data such as Web archives, newspaper archives, blogs, wikis, and micro-blogs. In these settings, searching and mining needs to use constraints on the time dimension in addition to keyword constraints. A natural approach to address such queries is using an inverted index whose entries are enriched with valid-time intervals. It has been shown that these indexes have to be partitioned along time in order to achieve efficiency. However, when the temporal predicate corresponds to a long time range, requiring the processing of multiple partitions, naive query processing incurs high cost of reading of redundant entries across partitions. We present a framework for efficient approximate processing of keyword queries over a temporally partitioned inverted index which minimizes this overhead, thus speeding up query processing. By using a small synopsis for each partition we identify partitions that maximize the number of final non-redundant results, and schedule them for processing early on. Our approach aims to balance the estimated gains in the final result recall against the cost of index reading required. We present practical algorithms for the resulting optimization problem of index partition selection. Our experiments with three diverse, large-scale text archives reveal that our proposed approach can provide close to 80% result recall even when only about half the index is allowed to be read.

/pdf/efficient-temporal-keyword-search-over-versioned-text-22d4utyov2.pdf

Srikanta Bedathur

Papers

EverLast: a distributed architecture for preserving the web

Search-Optimized suffix-tree storage for biological applications

Inferring and Exploiting Categories for Next Location Prediction

FluxCapacitor: Efficient Time-Travel Text Search

Efficient temporal keyword search over versioned text