OpenRaiser/Intern-Atlas huggingface dataset:
HDDBrs middleware for implementing highly available distributed databases
Authors: Rim Moussa
Venue: CIKM
Year: 2009

Our demo presents HDDBRS, a middle tier offering to clients a highly available distributed database interface using Reed Solomon codes to compute parity data. Parity data is stored in dedicated parity DB backends, is synchronously updated and allows recovering from multiple DB backend unavailability. HDDBRS middle tier is implemented in JAVA using standard technology, and is designed to be interoperable with any database engine that provides a JDBC driver and implements X/open XA protocol.
AS-index: a structure for string search using n-grams and algebraic signatures
Authors: Cédric du Mouza, Witold Litwin, Philippe Rigaux, Thomas J. E. Schwarz
Venue: CIKM
Year: 2009

AS-Index is a new index structure for exact string search in disk resident databases. It uses hashing, unlike known alternatives, whether baesd on trees or tries. It typically indexes every n-gram in the database, though non-dense indexing is possible. The hash function uses the algebraic signatures of n-grams. Use of hashing provides for constant index access time for arbitrarily long patterns, unlike other structures whose search cost is at best logarithmic. The storage overhead of AS-Index is basically 500 - 600%, similar to that ofalternatives or smaller. We show the index structure, our use of algebraic signatures and the search algorithm. We present the theoretical and experimental performance analysis. We compare the AS-Index to main alternatives. We conclude that AS-Index is an attractive structure and we indicate directions for future work.
Privacy and anonymization for very large datasets
Authors: Victor Muntés-Mulero, Jordi Nin
Venue: CIKM
Year: 2009

With the increase of available public data sources and the interest for analyzing them, privacy issues are becoming the eye of the storm in many applications. The vast amount of data collected on human beings and organizations as a result of cyberinfrastructure advances, or that collected by statistical agencies, for instance, has made traditional ways of protecting social science data obsolete. This has given rise to different techniques aimed at tackling this problem and at the analysis of limitations in such environments, such as the seminal study by Aggarwal of anonymization techniques and their dependency on data dimensionality. The growing accessibility to high-capacity storage devices allows keeping more detailed information from many areas. While this enriches the information and conclusions extracted from this data, it poses a serious problem for most of the previous work presented up to now regarding privacy, focused on quality and paying little attention to performance aspects. In this workshop, we want to gather researchers in the areas of data privacy and anonymization together with researchers in the area of high performance and very large data volumes management. We seek to collect the most recent advances in data privacy and anonymization (i.e. anonymization techniques, statistic disclosure techniques, privacy in machine learning algorithms, privacy in graphs or social networks, etc) and those in High Performance and Data Management (i.e. algorithms and structures for efficient data management, parallel or distributed systems, etc).
An outcome discovery system to determine mortality factors in primary care facilities
Authors: Jeremias Murillo, Min Song
Venue: CIKM
Year: 2009

This project assembles a virtual team consisting of personnel from the New Jersey Institute of Technology with expertise in the data mining domain and the Saint Barnabas Health Care System with expertise in the medical domain. We apply proven techniques in data and text mining to the problem of hospital mortality. Methodology in outcomes research using data/text mining has typically included Bayesian Networks to include decision trees and rules, regression analysis or Neural Networks/Support Vector Machines to analyze a single disease or condition. We propose to instead analyze the entire spectrum of reasons patients are admitted to a hospital in an effort to discern what chronologies result in good outcomes and which in the worst outcome so as to identify the characteristics to be avoided throughout the spectrum of reasons for admission.
Dynamic in-page logging for flash-aware B-tree index
Authors: Gap-Joo Na, Sang-Won Lee, Bongki Moon
Venue: CIKM
Year: 2009

This paper presents Dynamic IPL B+-tree (d-IPL in short) as a B+-tree index variant for flash-based storage systems. The d-IPL B+-tree adopts a dynamic In-Page Logging (IPL) scheme in order to address a few new problems that are caused by the unique characteristics of B+-tree indexes The d-IPL B+-tree avoids the frequent log overflow problem by allocating a log area in a flash block dynamically. It also addresses elegantly the problem of page evaporation, imposed by the contemporary NAND flash chips, by introducing ghost nodes within the context of the dynamic IPL scheme. This simple but elegant design of the d-IPL B+-tree improves the performance significantly. For a random insertion workload, the d-IPL B+-tree index outperformed a B+-tree with a plain IPL scheme by more than a factor of two in terms of page write and block erase operations.
Easiest-first search: towards comprehension-based web search
Authors: Makoto Nakatani, Adam Jatowt, Katsumi Tanaka
Venue: CIKM
Year: 2009

Although Web search engines have become information gateways to the Internet, for queries containing technical terms, search results often contain pages that are difficult to be understood by non-expert users. Therefore, re-ranking search results in a descending order of their comprehensibility should be effective for non-expert users. In our approach, the comprehensibility of Web pages is estimated considering both the document readability and the difficulty of technical terms in the domain of search queries. To extract technical terms, we exploit the domain knowledge extracted from Wikipedia. Our proposed method can be applied to general Web search engines as Wikipedia includes nearly every field of human knowledge. We demonstrate the usefulness of our approach by user experiments.
Incremental non-gaussian analysis of microarray gene expression data
Authors: Kam Swee Ng, Hyung-Jeong Yang, Sun-Hee Kim
Venue: CIKM
Year: 2009

The microarray is gaining popularity in biomedical research due to its ability to analyze hundreds to thousands of genes simultaneously in one experiment. However, the unique nature of microarray data, with a large number of features but relative small number of samples, poses challenges to process the microarray data effectively. The curse of dimensionality introduces the importance of feature extraction in analyzing microarray data. Therefore, we propose a novel incremental method to discover the non-Gaussian weight from the microarray gene expression data with high efficiency. Our proposed method can discover a small number of compact features from a huge number of genes and can still achieve good predictive performance. It integrates non-gaussianity and an adaptive incremental model in an unsupervised way to extract informative features. It is also plausible to analyze microarray data with the number of features much larger than number of observations with promising results.
Generating SQL/XML query and update statements
Authors: Matthias Nicola, Tim Kiefer
Venue: CIKM
Year: 2009

The XML support in relational databases and the SQL/XML language are still relatively new as compared to purely relational databases and traditional SQL. Today, most database users have a strong relational and SQL background. SQL/XML enables users to perform queries and updates across XML and relational data, but many struggle with writing SQL/XML statements or XQuery update expressions. One reason is the novelty of SQL/XML and of the XQuery expressions that must be included. Another problem is that the tree structure of the XML data may be unknown or difficult to understand for the user. Evolving XML Schemas as well as hybrid XML/relational schemas make it even harder to write SQL/XML statements. Also, legacy applications use SQL but may require access to XML data without costly code changes. Motivated by these challenges, we developed a method to generate SQL/XML query and update statements automatically. The input is either a GUI or a regular SQL statement that uses logical data item names irrespective of their actual location in relational or XML columns in the database. The output is a SQL/XML statement that queries or updates relational and XML data as needed to carry out the original user statement. This relieves the user and simplifies schema evolution and integration. We have prototyped and tested the proposed method on top of DB2 9.5.
Helping people to choose for whom to vote. a web information system for the 2009 European elections
Authors: Arjan Nusselder, Hendrike Peetz, Anne Schuth, Maarten Marx
Venue: CIKM
Year: 2009

We demonstrate a web information system created for the European elections in June 2009. Based on their speeches in the EU parliament and their written questions, we created language models for each of the 736 members of the EU parliament. These language models were used to search for politicians responsible for a given topic, similar to expert search applications. Users prefer to see some kind of evidence for returning a hit after a search. We created a profile of each EU parlementarian by comparing her personal language model to the language model created from all EU parlementarians. The top 50 words best separating the individual from the avarage were shown as a wordcloud. These top 50 words and their scores were derived from a parsimonious language model.
On privacy preservation in text and document-based active learning for named entity recognition
Authors: Fredrik Olsson
Venue: CIKM
Year: 2009

The preservation of the privacy of persons mentioned in text requires the ability to automatically recognize and identify names. Named entity recognition is a mature field and most current approaches are based on supervised machine learning techniques. Such learning requires the presence of labeled examples on which to train; training examples are usually provided to the learner on the form of annotated corpora. Creating and annotating corpora is a tedious, meticulous and error prone process; obtaining good training examples is a hard task in itself. This paper describes the development and in-depth empirical investigation of a method, called BootMark, for bootstrapping the marking up of named entities in textual documents. Experimental results show that BootMark requires a human annotator to manually annotate fewer documents in order to produce a named entity recognizer with a given performance, than would be needed if the documents forming the basis for the recognizer were randomly drawn from the same corpus. The investigation further indicates that the primary gain obtained by BootMark compared to passive learning is in terms of higher recall. Thus, it is argued, the recognizers are suitable for use in privacy preservation applications.
Feature selection for ranking using boosted trees
Authors: Feng Pan, Tim Converse, David Ahn, Franco Salvetti, Gianluca Donato
Venue: CIKM
Year: 2009

Modern search engines have to be fast to satisfy users, so there are hard back-end latency requirements. The set of features useful for search ranking functions, though, continues to grow, making feature computation a latency bottleneck. As a result, not all available features can be used for ranking, and in fact, much of the time, only a small percentage of these features can be used. Thus, it is crucial to have a feature selection mechanism that can find a subset of features that both meets latency requirements and achieves high relevance. To this end, we explore different feature selection methods using boosted regression trees, including both greedy approaches (selecting the features with highest relative importance as computed by boosted trees; discounting importance by feature similarity and a randomized approach. We evaluate and compare these approaches using data from a commercial search engine. The experimental results show that the proposed randomized feature selection with feature-importance-based backward elimination outperforms greedy approaches and achieves a comparable relevance with 30 features to a full-feature model trained with 419 features and the same modeling parameters.
Characterizing, constructing and managing resource usage profiles of system S applications: challenges and experience
Authors: Sujay S. Parekh, Kirsten Hildrum, Deepak Rajan, Joel L. Wolf, Kun-Lung Wu
Venue: CIKM
Year: 2009

We describe the challenges of characterizing, constructing and managing the usage profiles of System S applications. A running System S application is a directed graph with software processing elements(PEs) as vertices and data streams as edges connecting the PEs. The resource usage of each PE is a critical input to the runtime scheduler for proper resource allocation. We represent the resource usage of PEs in terms of resource functions (RFs) that are used by the System S scheduler, with one RF per resource per PE. The first challenge is that it is difficult to build good RFs that can accurately predict the resource usage of a PE because the PEs perform arbitrary computations. A second set of challenges arises in managing the RFs and performance data so that we can apply them for PEs that are re-run or reused by the same or different applications or users. We report our experience in overcoming these challenges. Specifically, we present an empirical characterization of PE RFs from several real streaming applications running in a System S testbed. This indicates that our simple models of resource usage that build on the data-flow nature of the underlying application can be effective, even for complex PEs. To illustrate our methodology, we evaluate and analyze the performance of these applications as a function of the quality of our resource profile models. The system automatically learns the models from the raw metrics data collected from running PEs. We describe our approach to managing the metrics and RF models, which allows us to construct generalizable RFs and eliminates the learning time for new PEs by intelligently storing and reusing the metrics data.
Towards real-time measurement of customer satisfaction using automatically generated call transcripts
Authors: Youngja Park, Stephen C. Gates
Venue: CIKM
Year: 2009

Customer satisfaction is a very important indicator of how successful a contact center is at providing services to the customers. Contact centers typically conduct a manual survey with a randomly selected group of customers to measure customer satisfaction. Manual customer satisfaction surveys, however, provide limited values due to high cost and the time lapse between the service and the survey.
Mining cancer genes with running-sum statistics
Authors: Inho Park, Kwang H. Lee, Doheon Lee
Venue: CIKM
Year: 2009

In this paper, we propose a new method to detect candidate cancer genes for developing molecular biomarkers or therapeutic targets from cancer microarray datasets. To resolve problems resulted in the molecular heterogeneity of cancers on gene prioritizing, our proposed method is intended to identify genes that are over- or down- expressed not in the whole cancer samples but also in a subgroup of cancer samples. To this end, we propose the RS score for gene ranking calculated with a weighted running sum statistic on the ordered list of expression values of each gene. We apply the proposed method to publically available prostate cancer microarray datasets, showing that it can identify previously well known prostate cancer associated genes such as ERG, HPN, and AMACR at the top of the list of candidate genes. Embedding samples, represented as vectors of the expression values of the top 20 genes, into a two dimensional space using the commute time embedding shows the distinction between normal samples and cancer samples in the independent test datasets as well as in the training datasets. We further evaluate the proposed method by estimating classification performance on the independent test datasets, and it shows the better classification performance compared to the other cancer outlier profile approaches.
Kernel latent semantic analysis using an information retrieval based kernel
Authors: Laurence Anthony F. Park, Kotagiri Ramamohanarao
Venue: CIKM
Year: 2009

Hidden term relationships can be found within a document collection using Latent semantic analysis (LSA) and can be used to assist in information retrieval. LSA uses the inner product as its similarity function, which unfortunately introduces bias due to document length and term rarity into the term relationships. In this article, we present the novel kernel based LSA method, which uses separate document and query kernel functions to compute document and query similarities, rather than the inner product. We show that by providing an appropriate kernel function, we are able to provide a better fit of our data and hence produce more effective term relationships.
M-COPE: a multiple continuous query processing engine
Authors: Hong Kyu Park, Se Jung Shin, Sang Hyuck Na, Won Suk Lee
Venue: CIKM
Year: 2009

A data stream management system (DSMS) should support an efficient evaluation scheme for long-running continuous queries over infinite data streams. This demonstration presents a scalable query processing engine, M-COPE (Multiple Continuous Query Processing Engine) developed to evaluate multiple continuous queries efficiently. A multiple query optimization scheme implemented in the system generates a single network of operations as an execution plan for registered queries in order to maximize the reuse of the intermediate results of common sub-expressions in the queries adaptively. In this paper, we describe the overall architecture of M-COPE along with its special features. Network traffic flow streams are used to demonstrate the main features of M-COPE.
ComprehEnRank: estimating comprehension in classroom by absorbing random walks on a cognitive graph
Authors: Nimit Pattanasri, Masayuki Mukunoki, Michihiko Minoh
Venue: CIKM
Year: 2009

This paper develops a graph-theoretic framework for estimating comprehension in classroom. To deal with imprecise data gathered in classroom, we propose multi-step comprehension propagation over a semantic graph. Random walks on the graph measure students' comprehension with probabilities absorbed at student nodes.
Framework for timely and accurate ads on mobile devices
Authors: Alex Penev, Raymond K. Wong
Venue: CIKM
Year: 2009

We propose a framework for mobile advertising covering Value-Added Services where an ad-database is maintained on the device and both selection and display are dictated by the device. Advantages over existing mobile marketing are that ads are more timely, viable on a variety of use cases, can be both location-sensitive and personalized with minimal privacy concerns, and provide an obvious means for subsidizing users' service costs. We construct a suitable selection algorithm and evaluate its execution, accuracy and scalability. We show that ad-serving can be done under the processing constraints imposed by mobiles, which may lead to improvements in mobile marketing effectiveness.
Time sequence summarization to scale up chronology-dependent applications
Authors: Quang-Khai Pham, Guillaume Raschia, Noureddine Mouaddib, Régis Saint-Paul, Boualem Benatallah
Venue: CIKM
Year: 2009

In this paper, we present the concept of Time Sequence Summarization to support chronology-dependent applications on massive data sources. Time sequence summarization takes as input a time sequence of events that are chronologically ordered. Each event is described by a set of descriptors. Time sequence summarization produces a concise time sequence that can be substituted for the original time sequence in chronology-dependent applications. We propose an algorithm that achieves time sequence summarization based on a generalization, grouping and concept formation process. Generalization expresses event descriptors at higher levels of abstraction using taxonomies while grouping gathers similar events. Concept formation is responsible for reducing the size of the input time sequence of events by representing each group created by one concept. The process is performed in a way such that the overall chronology of events is preserved. The algorithm computes the summary incrementally and has reduced algorithmic complexity. The resulting output is a concise representation, yet, informative enough to directly support chronology-dependent applications. We validate our approach by summarizing one year of financial news provided by Reuters.
Answer typing for information retrieval
Authors: Christopher Pinchak, Davood Rafiei, Dekang Lin
Venue: CIKM
Year: 2009

Answer typing is commonly thought of as finding appropriate responses to given questions. We extend the notion of answer typing to information retrieval to ensure results contain plausible answers to queries. Identification of a large class of applicable queries is performed using a discriminative classifier, and discriminative preference ranking methods are employed for the selection of type-appropriate terms. Experimental results show that type-appropriate terms identified by the model are superior to terms most commonly associated with the query, providing strong evidence that answer typing techniques can find meaningful and appropriate terms. Further experiments show that snippets containing correct answers are ranked higher by our model than by the baseline Google search engine in those instances in which a query does indeed seek a short answer.
Domain driven data mining to improve promotional campaign ROI and select marketing channels
Authors: Thomas Piton, Julien Blanchard, Henri Briand, Fabrice Guillet
Venue: CIKM
Year: 2009

The trading activities of materials retail is concerned with an extremely competitive market. However, business people are not well informed about how to proceed and what to do during marketing activities. Data mining methods could be interesting to generate substantial profits for decision makers and to optimize the choice of different marketing activities.
Mining tourist information from user-supplied collections
Authors: Adrian Popescu, Gregory Grefenstette, Pierre-Alain Moëllic
Venue: CIKM
Year: 2009

Tourist photographs constitute a large part of the images uploaded to photo sharing platforms. But filtering methods are needed before one can extract useful knowledge from noisy user-supplied metadata. Here we show how to extract clean trip related information (what people visit, for how long, panoramic spots) from Flickr metadata. We illustrate our technique on a sample of metadata and images covering 183 cities of different size and from different parts of the world.