waashk/dblp huggingface dataset:
a strategy for allowing meaningful and comparable scores in approximate matching. the goal of approximate data matching is to assess whether two distinct data instances represent the same real world object. this is usually achieved through the use of a similarity function, which returns a score that defines how similar two data instances are. if this score surpasses a given threshold, both data instances are considered as representing the same real world object. the score values returned by a similarity function depend on the algorithm that implements the function and have no meaning to the user (apart from the fact that a higher similarity value means that two data instances are more similar). in this paper, we propose that instead of defining the threshold in terms of the scores returned by a similarity function, the user specifies the precision that is expected from the matching process. precision is a well known quality measure and has a clear interpretation from the user's point of view. our approach relies on mapping between similarity scores and precision values based on a training data set. experimental results show the training may be executed against a representative data set, and reused for other databases from the same domain.
quickmig: automatic schema matching for data migration projects. a common task in many database applications is the migration of legacy data from multiple sources into a new one. this requires identifying semantically related elements of the source and target systems and the creation of mapping expressions to transform instances of those elements from the source format to the target format. currently, data migration is typically done manually, a tedious and timeconsuming process, which is difficult to scale to a high number of data sources. in this paper, we describe quickmig, a new semi-automatic approach to determining semantic correspondences between schema elements for data migration applications. quickmig advances the state of the art with a set of new techniques exploiting sample instances, domain ontologies, and reuse of existing mappings to detect not only element correspondences but also their mapping expressions. quickmig further includes new mechanisms to effectively incorporate domain knowledge of users into the matching process. the results from a comprehensive evaluation using real-world schemas and data indicate the high quality and practicability of the overall approach.
rk-hist: an r-tree based histogram for multi-dimensional selectivity estimation. database query engines typically rely upon query size estimators in order to evaluate the potential cost of alternate query plans. in multi-dimensional database systems, such as those typically found in large data warehousing environments, these selectivity estimators often take the form of multi-dimensional histograms. but while single dimensional histograms have proven to be quite accurate, even in the presence of data skew, the multi-dimensional variations have generally been far less reliable. in this paper, we present a new histogram model that is based upon an r-tree space partitioning. the localization of the r-tree boxes is in turn controlled by a hilbert space filling curve, while a series of efficient area equalization heuristics restructures the initial boxes to provide improved bucket representation. experimental results demonstrate significantly improved estimation accuracy relative to state of the art alternatives, as well as superior consistency across a variety of record distributions.
mapgraph: efficient methods for complex olap hierarchies. online analytical processing is a database paradigm that provides for the rich analysis of multi-dimensional data. olap is often supported by a logical structure known as the cube. however, supporting efficient olap query resolution in enterprise scale environments is an issue of considerable complexity. in practice, the difficulty of the problem is exacerbated by the existence of dimension hierarchies that sub-divide core dimensions into aggregation layers of varying granularity. common hierarchy-sensitive query operations such as rollup and drilldown can be very costly on large cubes. moreover, facilities for the representation of more complex hierarchical relationships are not well supported by conventional techniques. this paper presents a robust hierarchy infrastructure called mapgraph that supports the efficient and transparent manipulation of attribute hierarchies within olap environments. experimental results verify that, when compared to the alternatives, very little additional overhead is introduced, even when advanced functionality is exploited.
learning on the border: active learning in imbalanced data classification. this paper is concerned with the class imbalance problem which has been known to hinder the learning performance of classification algorithms. the problem occurs when there are significantly less number of observations of the target concept. various real-world classification tasks, such as medical diagnosis, text categorization and fraud detection suffer from this phenomenon. the standard machine learning algorithms yield better prediction performance with balanced datasets. in this paper, we demonstrate that active learning is capable of solving the class imbalance problem by providing the learner more balanced classes. we also propose an efficient way of selecting informative instances from a smaller pool of samples for active learning which does not necessitate a search through the entire dataset. the proposed method yields an efficient querying system and allows active learning to be applied to very large datasets. our experimental results show that with an early stopping criteria, active learning achieves a fast solution with competitive prediction performance in imbalanced data classification.
randomized metric induction and evolutionary conceptual clustering for semantic knowledge bases. we present an evolutionary clustering method which can be applied to multi-relational knowledge bases storing semantic resource annotations expressed in the standard languages for the semantic web. the method exploits an effective and language-independent semi-distance measure defined for the space of individual resources, that is based on a finite number of dimensions corresponding to a committee of features represented by a group of concept descriptions (discriminating features). we show how to obtain a maximally discriminating group of features through a feature construction method based on genetic programming. the algorithm represents the possible clusterings as strings of central elements (medoids, w.r.t. the given metric) of variable length. hence, the number of clusters is not needed as a parameter since the method can optimize it by means of the mutation operators and of a proper fitness function. we also show how to assign each cluster with a newly constructed intensional definition in the employed concept language. an experimentation with some ontologies proves the feasibility of our method and its effectiveness in terms of clustering validity indices.
evaluation of phrasal query suggestions. this paper evaluates the uptake and efficacy of a unified approach to phrasal query suggestions in the context of a high-precision search engine. the search engine performs ranked extended-boolean searches with the proximity operator <scp>near</scp> being the default operation. suggestions are offered to the searcher when the length of the result list falls outside predefined bounds. if the list is too long, the engine suggests narrowing the query through the use of super phrases; if the list is too short, the engine suggests broadening the query through the use of proximal subphrases. we evaluated uptake of phrasal query suggestions by analyzing search log data from before and after the suggestion feature was added to a commercial version of the search engine. we looked at approximately 1.5 million queries and found that, after they were added, suggestions represented nearly 30% of the total queries. we evaluated efficacy through a controlled study of 24 participants performing nine searches using three different search engines. we found that the engine with phrase suggestions had better high-precision recall than both the same search engine without suggestions and a search engine with a similar interface but using an okapi bm25 ranking algorithm.
improving the classification of newsgroup messages through social network analysis. improving the classification of newsgroup messages through social network analysis. in this paper, we focus on automatic classification of message replies into several types. for representing messages we consider rich feature sets that combine the standard author reply-to network properties with features derived from four additional structures identified in the data: 1) a network of authors who participate in the same threads, 2) network of authors who post similar content, 3) network of threads sharing common authors, and 4) network of content-related threads. for selected newsgroups we train linear svm classifiers to identify agreement and disagreement with the original message, and question and answer patterns in the threads. we show that the use of newly defined features substantially improves classification of messages in comparison with the svm model based only on the standard reply-to network.
optimizing parallel itineraries for knn query processing in wireless sensor networks. spatial queries for extracting data from wireless sensor networks are important for many applications, such as environmental monitoring and military surveillance. one such query is k nearest neighbor (knn) query that facilitates sampling of monitored sensor data in correspondence with a given query location. recently, itinerary-based knn query processing techniques, that propagate queries and collect data along a pre-determined itinerary, have been developed concurrently [12] [14]. these research works demonstrate that itinerary-based knn query processing algorithms are able to achieve better energy efficiency than other existing algorithms. however, how to derive itineraries based on different performance requirements remains a challenging problem. in this paper, we propose a new itinerary-based knn query processing technique, called pciknn, that derives different itineraries aiming at optimizing two performance criteria, response latency and energy consumption. the performance of pciknn is analyzed mathematically and evaluated through extensive experiments. experimental results show that pciknn has better performance and scalability than the state-of-the-art.
ntjfsatnot: a novel method for query with not-predicates on xml data. previous researches, in the filed of xml databases, have been done to evaluate xml queries with and-branches. however, as far as we know, very little work has examined the efficient processing of xml queries with not-predicates. also these methods have to process all of the query nodes in the document when dealing with queries with not-branch. in this paper, with some modification in tjfast method, we propose a new manner for answering to various not-queries. this method processes nodes efficiently, in a way that in the ideal state, we obtain part of the answer after the process of each node, and we don't have any unreasonable processing of each node.
efficient on-line index maintenance for dynamic text collections by using dynamic balancing tree. previous on-line index maintenance strategies are mainly designed for document insertions without considering document deletions. in a truly dynamic search environment, however, documents may be added to and removed from the collection at any point in time. in this paper, we examine issues of on-line index maintenance with support for instantaneous document deletions and insertions. we present a dbt merge strategy that can dynamically adjust the sequence of sub-index merge operations during index construction, and offers better query processing performance than previous methods, while providing an equivalent level of index maintenance performance when document insertions and deletions exist in parallel. using experiments on 426 gb of web data we demonstrate the efficiency of our method in practice, showing that on-line index construction for dynamic text collections can be performed efficiently and almost as fast as for growing text collections.
combination of evidences in relevance feedback for xml retrieval. 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.
a correlation-based model for unsupervised feature selection. 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.
measuring article quality in wikipedia: models and evaluation. wikipedia has grown to be the world largest and busiest free encyclopedia, in which articles are collaboratively written and maintained by volunteers online. despite its success as a means of knowledge sharing and collaboration, the public has never stopped criticizing the quality of wikipedia articles edited by non-experts and inexperienced contributors. in this paper, we investigate the problem of assessing the quality of articles in collaborative authoring of wikipedia. we propose three article quality measurement models that make use of the interaction data between articles and their contributors derived from the article edit history. our b<scp>asic</scp> model is designed based on the mutual dependency between article quality and their author authority. the p<scp>eer</scp>r<scp>eview</scp> model introduces the review behavior into measuring article quality. finally, our p<scp>rob</scp>r<scp>eview</scp> models extend p<scp>eer</scp>r<scp>eview</scp> with partial reviewership of contributors as they edit various portions of the articles. we conduct experiments on a set of well-labeled wikipedia articles to evaluate the effectiveness of our quality measurement models in resembling human judgement.
comments-oriented blog summarization by sentence extraction. much existing research on blogs focused on posts only, ignoring their comments. our user study conducted on summarizing blog posts, however, showed that reading comments does change one's understanding about blog posts. in this research, we aim to extract representative sentences from a blog post that best represent the topics discussed among its comments. the proposed solution first derives representative words from comments and then selects sentences containing representative words. the representativeness of words is measured using requt (i.e., reader, quotation, and topic). evaluated on human labeled sentences, requt together with summation-based sentence selection showed promising results.
a method for online analytical processing of text data. 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.
ix-cubes: iceberg cubes for data warehousing and olap on xml data. 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.
a two-stage approach to domain adaptation for statistical classifiers. in this paper, we consider the problem of adapting statistical classifiers trained from some source domains where labeled examples are available to a target domain where no labeled example is available. one characteristic of such a domain adaptation problem is that the examples in the source domains and the target domain are known to follow different distributions. thus a regular classification method would tend to overfit the source domains. we present a two-stage approach to domain adaptation, where at the first <generalization stage, we look for a set of features generalizable across domains, and at the second adaptation stage, we pick up useful features specific to the target domain. observing that the exact objective function is hard to optimize, we then propose a number of heuristics to approximately achieve the goal of generalization and adaptation. our experiments on gene name recognition using a real data set show the effectiveness of our general framework and the heuristics.
"i know what you did last summer": query logs and user privacy. 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. we also present a new attack in which a real-world acquaintance of a user attempts to identify that user in a large query log, using personal information. we show that combinations of small pieces of information about terms a user would probably search for can be highly effective in identifying the sessions of that user. we conclude that known schemes to release even heavily scrubbed query logs that contain session information have significant privacy risks.
indexing multiversion databases. 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.
discovering authorities in question answer communities by using link analysis. question-answer portals such as naver and yahoo! answers are quickly becoming rich sources of knowledge on many topics which are not well served by general web search engines. unfortunately, the quality of the submitted answers is uneven, ranging from excellent detailed answers to snappy and insulting remarks or even advertisements for commercial content. furthermore, user feedback for many topics is sparse, and can be insufficient to reliably identify good answers from the bad ones. hence, estimating the authority of users is a crucial task for this emerging domain, with potential applications to answer ranking, spam detection, and incentive mechanism design. we present an analysis of the link structure of a general-purpose question answering community to discover authoritative users, and promising experimental results over a dataset of more than 3 million answers from a popular community qa site. we also describe structural differences between question topics that correlate with the success of link analysis for authority discovery.
parallel linkage. 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.
generalizing from relevance feedback using named entity wildcards. traditional adaptive filtering systems learn the user's interests in a rather simple way - words from relevant documents are favored in the query model, while words from irrelevant documents are down-weighted. this biases the query model towards specific words seen in the past, causing the system to favor documents containing relevant but redundant information over documents that use previously unseen words to denote new facts about the same news event. this paper proposes news ways of generalizing from relevance feedback by augmenting the traditional bag-of-words query model with named entity wildcards that are anchored in context. the use of wildcards allows generalization beyond specific words, while contextual restrictions limit the wildcard-matching to entities related to the user's query. we test our new approach in a nugget-level adaptive filtering system and evaluate it in terms of both relevance and novelty of the presented information. our results indicate that higher recall is obtained when lexical terms are generalized using wildcards. however, such wildcards must be anchored to their context to maintain good precision. how the context of a wildcard is represented and matched against a given document also plays a crucial role in the performance of the retrieval system.