OpenRaiser/Intern-Atlas huggingface dataset:
Combination of evidences in relevance feedback for xml retrieval
Authors: Lobna Hlaoua, Mohand Boughanem, Karen Pinel-Sauvagnat
Venue: CIKM
Year: 2007

The main objective in XML Retrieval is to select the relevant elements of XML document instead of the whole document. Many open issues appear when considering Relevance Feedback (RF) in XML documents. They are mainly related to the form of XML documents, which mix content and structure information and to the new information granularity. In this paper, a new flexible method of relevance feedback in XML retrieval using two sources of evidence is described. We propose to use the context criterion to select terms to extend the initial query and to use generative structures to express structural constraints. Both approaches are applied in different combined forms. Experiments are carried out with the INEX evaluation campaign and results show the effectiveness of our approach.
Towards workload shift detection and prediction for autonomic databases
Authors: Marc Holze, Norbert Ritter
Venue: CIKM
Year: 2007

Due to the complexity of industry-scale database systems, the total cost of ownership for these systems is no longer dominated by hardware and software, but by administration expenses. Autonomic databases intend to reduce these costs by providing self-management features. Existing approaches towards this goal are supportive advisors for the database administrator and feedback control loops for online monitoring, analysis and re-configuration. But while advisors are too resource-consuming for continuous operation, feedback control loops suffer from overreaction, oscillation and interference.
A correlation-based model for unsupervised feature selection
Authors: Michael E. Houle, Nizar Grira
Venue: CIKM
Year: 2007

We propose a new model for feature evaluation and selection that assesses the propensity of the features to support two-set classification. For each item of the data set, the collection of features induce a ranking (ordered list) of the remaining items. The evaluation criterion favors features that result in the most consistent discrimination between relevant and non-relevant items within these ranked lists. The discrimination boundaries within a single list are determined combinatorially, according to the degree of correlation among the relevant sets of its members. The model makes no special assumptions on the nature of the data. A selection heuristic based on the model is also proposed using sequential forward generation, and an experimental comparison is made with other unsupervised feature selection methods.
A method for online analytical processing of text data
Authors: Akihiro Inokuchi, Koichi Takeda
Venue: CIKM
Year: 2007

There are increasingly visible demands for structured/ unstructured information integration and advanced analytics. However, conventional database technology has not been able to present a robust and practical implementation of a truly integrated architecture for such purposes. After working on several industrial applications (in particular, in the healthcare and life sciences area), we have identified fundamental issues and technical approaches to tackle the issues. In this paper, we propose data representations and algebraic operations for integrating semantic information (e.g., ontologies) into OLAP systems, which allow us to analyze a huge set of textual documents with their underlying semantic information. The performance of the prototype implementation has been evaluated using real world datasets, and the high scalability and flexibility of our approach have been confirmed with respect to the computation time.
A conceptual modeling and execution framework for process based scientific applications
Authors: Stefan Jablonski, Bernhard Volz, M. Abdul Rehman
Venue: CIKM
Year: 2007

In recent years, scientists are dealing more and more with data intensive and complex applications. Many scientific workflow systems emerged which adapt technology and methods stemming from the workflow management area and that should support scientists in understanding and working with their complex scenarios. However as these systems often descend from problem solving environments, many of them are missing a well structured conceptual method for process modeling and execution as a foundation. In this publication we present a comprehensive and well structured method for developing and analyzing process based scientific applications. This method is constituted by a process modeling framework, a data integration framework and a model driven approach to build up infrastructures for process modeling and execution.
Ix-cubes: iceberg cubes for data warehousing and olap on xml data
Authors: Fianny Ming-fei Jiang, Jian Pei, Ada Wai-Chee Fu
Venue: CIKM
Year: 2007

With increasing amount of data being stored in XML format, OLAP queries over these data become important. OLAP queries have been well studied in the relational database systems. However, the evaluation of OLAP queries over XML data is not a trivial extension of the relational solutions, especially when a schema is not available. In this paper, we introduce the IX-cube (Iceberg XML cube) over XML data to tackle the problem. We extend OLAP operations to XML data. We also develop efficient approaches to IX-Cube computation and OLAP query evaluation using IX-cubes.
"I know what you did last summer": query logs and user privacy
Authors: Rosie Jones, Ravi Kumar, Bo Pang, Andrew Tomkins
Venue: CIKM
Year: 2007

We investigate the subtle cues to user identity that may be exploited in attacks on the privacy of users in web search query logs. We study the application of simple classifiers to map a sequence of queries into the gender, age, and location of the user issuing the queries. We then show how these classifiers may be carefully combined at multiple granularities to map a sequence of queries into a set of candidate users that is 300-600 times smaller than random chance would allow. We show that this approach remains accurate even after removing personally identifiable information such as names/numbers or limiting the size of the query log.
Indexing multiversion databases
Authors: Khaled Jouini, Geneviève Jomier
Venue: CIKM
Year: 2007

An efficient management of multiversion data with branched evolution is crucial for many applications. It requires database designers aware of tradeoffs among index structures and policies. This paper defines a framework and an analysis method for understanding the behavior of different indexing policies. Given data and query characteristics the analysis allows determining the most suitable index structure. The analysis is validated by an experimental study.
Combining resources with confidence measures for cross language information retrieval
Authors: Youssef Kadri, Jian-Yun Nie
Venue: CIKM
Year: 2007

Query translation in Cross Language Information Retrieval (CLIR) can be performed using multiple resources. Previous attempts to combine different translation resources use simple methods such as linear combination. Unfortunately, these approaches are insufficient to combine different types of resources such as bilingual dictionaries and statistical translation models. In this paper, we use confidence measures for this combination for the purpose of English-Arabic CLIR. Confidence measure is used to adjust the original scores of translations and to create a weight of the same nature for translations with different resources. We tested this technique on two test CLIR collections from TREC and obtained encouraging improvements compared to the results of linear combination.
Parallel linkage
Authors: Hung-sik Kim, Dongwon Lee
Venue: CIKM
Year: 2007

We study the parallelization of the (record) linkage problem - i.e., to identify matching records between two collections of records, A and B. One of main idiosyncrasies of the linkage problem, compared to Database join, is the fact that once two records a in A and b in B are matched and merged to c, c needs to be compared to the rest of records in A and B again since it may incur new matching. This re-feeding stage of the linkage problem requires its solution to be iterative, and complicates the problem significantly. Toward this problem, we first discuss three plausible scenarios of inputs - when both collections are clean, only one is clean, and both are dirty. Then, we show that the intricate interplay between match and merge can exploit the characteristics of each scenario to achieve good parallelization. Our parallel algorithms achieve 6.55-7.49 times faster in speedup compared to sequential ones with 8 processors, and 11.15-18.56% improvement in efficiency compared to P-Swoosh.
Incorporating quality aspects in sensor data streams
Authors: Anja Klein
Venue: CIKM
Year: 2007

Sensors are increasingly embedded into physical products in order to capture data about their conditions and usage for decision making in business applications. However, a major issue for such applications is the limited quality of the captured data due to inherently restricted precision and performance of the sensors. Moreover, the data quality is further decreased by data processing to meet resource constraints in streaming environments and ultimately influences business decisions. The issue of how to efficiently provide applications with information about data quality (DQ) is still an open research problem.
Maxprob and categorization of queries based on linguistic features
Authors: Désiré Kompaoré, Josiane Mothe
Venue: CIKM
Year: 2007

In this paper, we study the use of a probabilistic fusion approach inspired from the probFuse algorithm [14]. Our fusing technique is based on queries that are classified using some automatically extracted linguistic features [11]. In this approach, performances which are estimated during a training phase are used as an indicator of the systems to fuse. The rank list of relevant documents provided by the systems are divided into segments and used in a training/testing process to detect the systems to fuse. Our fusion technique shows better performances than other fusion techniques like CombMNZ [10], ProbFuse [14] and Best_sys [11].
Selective user interaction
Authors: Giridhar Kumaran, James Allan
Venue: CIKM
Year: 2007

poster Share on Selective user interaction Authors: Giridhar Kumaran University of Massachusetts Amherst, Amherst, MA University of Massachusetts Amherst, Amherst, MAView Profile , James Allan University of Massachusetts Amherst, Amherst, MA University of Massachusetts Amherst, Amherst, MAView Profile Authors Info & Claims CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge managementNovember 2007 Pages 923–926https://doi.org/10.1145/1321440.1321576Online:06 November 2007Publication History 8citation210DownloadsMetricsTotal Citations8Total Downloads210Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Finding dense and isolated submarkets in a sponsored search spending graph
Authors: Kevin J. Lang, Reid Andersen
Venue: CIKM
Year: 2007

Methods for improving sponsored search revenue are often tested or deployed within a small submarket of the larger marketplace. For many applications, the ideal submarket contains a small number of nodes, a large amount of spending within the submarket, and a small amount of spending leaving the submarket. We introduce an efficient algorithm for finding submarkets that are optimal for a user-specified tradeoff between these three quantities. We apply our algorithm to find submarkets that are both dense and isolated in a large spending graph from Yahoo! sponsored search.
Natural language processing for information retrieval: the time is ripe (again)
Authors: Matthew Lease
Venue: CIKM
Year: 2007

Paraphrasing van Rijsbergen [37], the time is ripe for another attempt at using natural language processing (NLP) for information retrieval (IR). This paper introduces my dissertation study, which will explore methods for integrating modern NLP with state-of-the-art IR techniques. In addition to text, I will also apply retrieval to conversational speech data, which poses a unique set of considerations in comparison to text. Greater use of NLP has potential to improve both text and speech retrieval.
Structure-based inference of xml similarity for fuzzy duplicate detection
Authors: Luís Leitão, Pável Calado, Melanie Weis
Venue: CIKM
Year: 2007

Fuzzy duplicate detection aims at identifying multiple representations of real-world objects stored in a data source, and is a task of critical practical relevance in data cleaning, data mining, or data integration. It has a long history for relational data stored in a single table (or in multiple tables with equal schema). Algorithms for fuzzy duplicate detection in more complex structures, e.g., hierarchies of a data warehouse, XML data, or graph data have only recently emerged. These algorithms use similarity measures that consider the duplicate status of their direct neighbors, e.g., children in hierarchical data, to improve duplicate detection effectiveness. In this paper, we propose a novel method for fuzzy duplicate detection in hierarchical and semi-structured XML data. Unlike previous approaches, it not only considers the duplicate status of children, but rather the probability of descendants being duplicates. Probabilities are computed efficiently using a Bayesian network. Experiments show the proposed algorithm is able to maintain high precision and recall values, even when dealing with data containing a high amount of errors and missing information. Our proposal is also able to outperform a state-of-the-art duplicate detection system on three different XML databases.
Just in time indexing for up to the second search
Authors: Ronny Lempel, Yosi Mass, Shila Ofek-Koifman, Dafna Sheinwald, Yael Petruschka, Ron Sivan
Venue: CIKM
Year: 2007

E-commerce and intranet search systems require newly arriving content to be indexed and made available for search within minutes or hours of arrival. Applications such as file system and email search demand even faster turnaround from search systems, requiring new content to become available for search almost instantaneously. However, incrementally updating inverted indices, which are the predominant datastructure used in search engines, is an expensive operation that most systems avoid performing at high rates.
Diva: a variance-based clustering approach for multi-type relational data
Authors: Tao Li, Sarabjot S. Anand
Venue: CIKM
Year: 2007

Clustering is a common technique used to extract knowledge from a dataset in unsupervised learning. In contrast to classical propositional approaches that only focus on simple and flat datasets, relational clustering can handle multi-type interrelated data objects directly and adopt semantic information hidden in the linkage structure to improve the clustering result. However, exploring linkage information will greatly reduce the scalability of relational clustering. Moreover, some characteristics of vector data space utilized to accelerate the propositional clustering procedure are no longer valid in relational data space. These two disadvantages restrain the relational clustering techniques from being applied to very large datasets or in time-critical tasks, such as online recommender systems. In this paper we propose a new variance-based clustering algorithm to address the above difficulties. Our algorithm combines the advantages of divisive and agglomerative clustering paradigms to improve the quality of cluster results. By adopting the idea of Representative Object, it can be executed with linear time complexity. Experimental results show our algorithm achieves high accuracy, efficiency and robustness in comparison with some well-known relational clustering approaches.
Effective keyword search for valuable lcas over xml documents
Authors: Guoliang Li, Jianhua Feng, Jianyong Wang, Lizhu Zhou
Venue: CIKM
Year: 2007

In this paper, we study the problem of effective keyword search over XML documents. We begin by introducing the notion of Valuable Lowest Common Ancestor (VLCA) to accurately and effectively answer keyword queries over XML documents. We then propose the concept of Compact VLCA (CVLCA) and compute the meaningful compact connected trees rooted as CVLCAs as the answers of keyword queries. To efficiently compute CVLCAs, we devise an effective optimization strategy for speeding up the computation, and exploit the key properties of CVLCA in the design of the stack-based algorithm for answering keyword queries. We have conducted an extensive experimental study and the experimental results show that our proposed approach achieves both high efficiency and effectiveness when compared with existing proposals.
Predicting individual priorities of shared activities using support vector machines
Authors: Lida Li, Michael J. Muller, Werner Geyer, Casey Dugan, Beth Brownholtz, David R. Millen
Venue: CIKM
Year: 2007

Activity-centric collaboration environments help knowledge workers to manage the context of their shared work activities by providing a representation for an activity and its resources. Activity management systems provide more structure and organization than email to execute the shared activity but, as the number of shared activities increases, it becomes more and more difficult for users to focus on important activities that need their attention. This paper describes a personalized activity prioriti-zation approach implemented on top of the Lotus Connections Activities management system. Our prototype implementation allows each user to view activities ordered by her/his predicted priorities. The predictions are made using a ranking Support Vector Machine model trained with the user’s past interactions with the activities system. We describe the prioritization interface and the results of an offline experiment based on data from 13 users over 6-months. Our results show that our feature set derived from shared activity structures can significantly increase prediction accuracy compared to a recency baseline.
Ensembling Bayesian network structure learning on limited data
Authors: Feng Liu, Fengzhan Tian, QiLiang Zhu
Venue: CIKM
Year: 2007

In recent years, Bagging method has been applied to learn Bayesian networks (BNs), especially on limited datasets. However, the BNs learned using Bagging method from limited datasets can be biased towards complex models. We present an efficient approach to produce more accurate BNs from limited datasets. Based on the Markov condition of BN learning, we proposed a novel sampling method, called Root Nodes based Sampling (RNS), and a BNs fusion method. The experimental results reveal that our ensemble method can achieve more accurate results in terms of accuracy on limited datasets.
Visualization and analysis of large graphs
Authors: Eloïse Loubier, Wahiba Bahsoun, Bernard Dousset
Venue: CIKM
Year: 2007

In Knowledge engineering, synthesized information has often an evolving and relational form. Information representation using graphs may ease data interpretation for non-expert users. However this graph may be complex and simplifications are useful in order to ease analysis. In this article, we present VisuGraph, a powerful tool for graph drawing. This tool gives the possibility to reduce large graph by two techniques: the Markov CLustering algorithm (MCL) application and the global graph division in time-sliced visualizations in order to specify and to simplify temporal analysis.
Reusing relational sources for semantic information access
Authors: Lina Lubyte
Venue: CIKM
Year: 2007

The rapid growth of available data arises the need for more sophisticated techniques for semantic access to information. It has been proved that using conceptual model or ontology over relational data sources is necessary to overcome many problems related with accessing the structured data. However, the task of wrapping the data residing in a database by means of an ontology is mainly done manually.
CTO: concept tree based semantic overlay for pure peer-to-peer information retrieval
Authors: Jianming Lv, Xueqi Cheng
Venue: CIKM
Year: 2007

Inspired by how search behavior works in human society, we propose CTO, a self-organized semantic overlay based on concept tree for P2P IR infrastructure, which is efficient for full text search in pure P2P environment without any central control or powerful peer as hub node. Especially, CTO performs very well on searching the unpopular resources shared by a few peers. In our experiment, while searching for the scarce documents shared by the peers, CTO achieves about 80% recall rate when the search covers less than 5% peers in the overlay. The search latency of CTO is also very low, which is controlled in the range about 5~12 hops.
Personal digital library: pim through a 5s perspective
Authors: Yi Ma, Edward A. Fox, Marcos André Gonçalves
Venue: CIKM
Year: 2007

Information overload and fragmentation problems result from the huge volume of data available in digital formats. This has led to increasing interest in personal information management research. However, current efforts have not fully clarified the thinking in this area due to the lack of theoretical foundations. In this paper, we present a formal framework for personal information management through a personal digital library perspective based upon the 5S approach. This framework includes a formal definition of the components and functionalities for a minimal personal digital library. We also present the primary testing results of our initial implementation of receptor module and behavioral information relevance. Challenges expected in planned future efforts are discussed too, with suggestions for possible solutions. We believe this is one of the first efforts to provide a theoretical foundation for personal information management. Our results and planned work show promise for further doctoral research.
High-performance distributed inverted files
Authors: Mauricio Marín, Veronica Gil-Costa
Venue: CIKM
Year: 2007

We present a general method of parallel query processing that allows scalable performance on distributed inverted files. The method allows the realization of a hybrid that combines the advantages of the document and term partitioned inverted files.
Dex: high-performance exploration on large graphs for information retrieval
Authors: Norbert Martínez-Bazan, Victor Muntés-Mulero, Sergio Gómez-Villamor, Jordi Nin, Mario-A. Sánchez-Martínez, Josep Lluís Larriba-Pey
Venue: CIKM
Year: 2007

Link and graph analysis tools are important devices to boost the richness of information retrieval systems. Internet and the existing social networking portals are just a couple of situations where the use of these tools would be beneficial and enriching for the users and the analysts. However, the need for integrating different data sources and, even more important, the need for high performance generic tools, is at odds with the continuously growing size and number of data repositories. In this paper we propose and evaluate DEX, a high performance graph database querying system that allows for the integration of multiple data sources. DEX makes graph querying possible in different flavors, including link analysis, social network analysis, pattern recognition and keyword search. The richness of DEX shows up in the experiments that we carried out on the Internet Movie Database(IMDb). Through a variety of these complex analytical queries, DEX shows to be a generic and efficient tool on large graph databases.