waashk/dblp huggingface dataset:
multi-aspect expertise matching for review assignment. review assignment is a common task that many people such as conference organizers, journal editors, and grant administrators would have to do routinely. as a computational problem, it involves matching a set of candidate reviewers with a paper or proposal to be reviewed. a common deficiency of all existing work on solving this problem is that they do not consider the multiple aspects of topics or expertise and all match the entire document to be reviewed with the overall expertise of a reviewer. as a result, if a document contains multiple subtopics, which often happens, existing methods would not attempt to assign reviewers to cover all the subtopics; instead, it is quite possible that all the assigned reviewers would cover the major subtopic quite well, but not covering any other subtopic. in this paper, we study how to model multiple aspects of expertise and assign reviewers so that they together can cover all subtopics in the document well. we propose three general strategies for solving this problem and propose new evaluation measures for this task. we also create a multi-aspect review assignment test set using acm sigir publications. experiment results on this data set show that the proposed methods are effective for assigning reviewers to cover all topical aspects of a document.
overlapping community structure detection in networks. many systems in nature and human society take the form of networks with community structures. in this paper, we describe a simple algorithm cocd(clique-based overlapping community detection) to efficiently mine the overlapping communities in large-scale networks, which is useful for us to have a better understanding of the nested sub-structures embedded in the whole network.
e-discovery. it is common practice in the u.s. for courts to require that the parties to a legal case make available to one another all material relevant to the case, including electronically held data and documents. for large corporations, such relevant information may encompass terabytes of e-mail and other files spanning many years. the challenge of e-discovery in response to a court order is (in a relatively short amount of time) to identify, assemble, individuate, access, categorize, and analyze an organization's electronically held material, segregate all "privileged" material (which can be legally withheld), and present to the court all (and only) the required documents. the techniques needed to accomplish such a task necessarily include search, clustering, classification, filtering, social network analysis, extraction, and more - and no one of these is sufficient. the panel will describe this problem in detail, providing additional background and context on e-discovery generally, and will explore specific techniques and cases that amply demonstrate why e-discovery is quintessentially a "cikm" problem -- multi-disciplinary, multi-technology, and "multi-difficult".
large maximal cliques enumeration in sparse graphs. here we study a variant of maximal clique enumeration problem by incorporating a minimum size criterion. we describe preprocessing techniques to reduce the graph size. this is of practical interest since enumerating maximal cliques is a computationally hard problem and the execution time increases rapidly with the input size. we discuss basics of an algorithm for enumerating large maximal cliques which exploits the constraint on minimum size of the desired maximal cliques. social networks are prime examples of large sparse graphs where enumerating large maximal cliques is of interest. we present experimental results on the social network formed by the call detail records of one of the world's largest telecom service providers. our results show that the preprocessing methods achieve significant reduction in the graph size. we also characterize the execution behaviour of our large maximal clique enumeration algorithm.
proactive learning: cost-sensitive active learning with multiple imperfect oracles. proactive learning is a generalization of active learning designed to relax unrealistic assumptions and thereby reach practical applications. active learning seeks to select the most informative unlabeled instances and ask an omniscient oracle for their labels, so as to retrain the learning algorithm maximizing accuracy. however, the oracle is assumed to be infallible (never wrong), indefatigable (always answers), individual (only one oracle), and insensitive to costs (always free or always charges the same). proactive learning relaxes all four of these assumptions, relying on a decision-theoretic approach to jointly select the optimal oracle and instance, by casting the problem as a utility optimization problem subject to a budget constraint. results on multi-oracle optimization over several data sets demonstrate the superiority of our approach over the single-imperfect-oracle baselines in most cases.
dual encryption for query integrity assurance. in database outsourcing, an enterprise contracts its database management tasks to an outside database service provider to eliminate in-house hardware, software, and expertise needs for running dbmss. this is attractive especially for the parties with limited abilities in managing their own data. typically, the client applications want to obtain quality assurance (e.g., data authenticity and query completeness) of the outsourced database service at a low cost. previous work on database outsourcing has focused on issues such as communication overhead, secure data access, and data privacy. recent work has introduced the issue of query integrity assurance, but usually, to obtain such assurance incurs a high cost. in this paper, we present a new method called dual encryption to provide low-cost query integrity assurance for outsourced database services. dual encryption enables "cross examination" of the outsourced data, which consists of the original data stored under a certain encryption scheme, and another small percentage of the original data stored under a different encryption scheme. we generate queries against the additional piece of data and analyze their results to obtain integrity assurance. our scheme is provable secure, that is, it is impossible to break our scheme unless some security primitives can be broken. experiments on commercial workloads show the effectiveness of our approach.
search advertising using web relevance feedback. the business of web search, a $10 billion industry, relies heavily on sponsored search, whereas a few carefully-selected paid advertisements are displayed alongside algorithmic search results. a key technical challenge in sponsored search is to select ads that are relevant for the user's query. identifying relevant ads is challenging because queries are usually very short, and because users, consciously or not, choose terms intended to lead to optimal web search results and not to optimal ads. furthermore, the ads themselves are short and usually formulated to capture the reader's attention rather than to facilitate query matching. traditionally, matching of ads to queries employed standard information retrieval techniques using the bag of words approach. here we propose to go beyond the bag of words, and augment both queries and ads with additional knowledge-rich features. we use web search results initially returned for the query to create a pool of relevant documents. classifying these documents with respect to an external taxonomy and identifying salient named entities give rise to two new feature types. empirical evaluation based on over 9,000 query-ad pairwise judgments confirms that using augmented queries produces highly relevant ads. our methodology also relaxes the requirement for each ad to explicitly specify the exhaustive list of queries ("bid phrases") that can trigger it.
fast mining of complex time-stamped events. given a collection of complex, time-stamped events, how do we find patterns and anomalies? events could be meetings with one or more persons and one or more agenda items at zero or more locations (e.g., teleconferences), or they could be publications with authors, keywords, publishers, etc. in such settings, we want to find time stamps that look similar to each other and group them; we also want to find anomalies. in addition, we want our approach to provide interpretations of the clusters and anomalies by annotating them. furthermore, we want our approach to automatically find the right time-granularity in which to do analysis. lastly, we want fast, scalable algorithms for all these problems. we address the above challenges through two main ideas. the first (t3) is to turn the problem into a graph analysis problem, by carefully treating each time stamp as a node in a graph. this viewpoint brings to bear the vast machinery of graph analysis methods (pagerank, graph partitioning, proximity analysis, and centerpiece subgraphs, to name a few). thus, t3 can automatically group the time stamps into meaningful clusters and spot anomalies. moreover, it can select representative events/persons/locations for each cluster and each anomaly, as their interpretations. the second idea (mt3) is to use temporal multi-resolution analysis (e.g., minutes, hours, days). we show that mt3 can quickly derive results from finer-to-coarser resolutions, achieving up to 2 orders of magnitude speedups. we verify the effectiveness as well as efficiency of t3 and mt3 on several real datasets.
records retention in relational database systems. the recent introduction of several pieces of legislation mandating minimum and maximum retention periods for corporate records has prompted the enterprise content management (ecm) community to develop various records retention solutions. records retention is a significant subfield of records management, and legal records retention requirements apply over corporate records regardless of their shape or form. unfortunately, the scope of existing solutions has been largely limited to proper identification, classification and retention of documents, and not of data more generally. in this paper we address the problem of managed records retention in the context of relational database systems. the problem is significantly more challenging than it is for documents for several reasons. foremost, there is no clear definition of what constitutes a business record in relational databases; it could be an entire table, a tuple, part of a tuple, or parts of several tuples from multiple tables. there are also no standardized mechanisms for purging, anonymizing and protecting relational records. functional dependencies, user defined constraints, and side effects caused by triggers make it even harder to guarantee that any given record will actually be protected when it needs to be protected or expunged when the necessary conditions are met. most importantly, relational tuples may be organized such that one piece of data may be part of various legal records and subject to several (possibly conflicting) retention policies. we address the above problems and present a complete solution for designing, managing, and enforcing records retention policies in relational database systems. we experimentally demonstrate that the proposed framework can guarantee compliance with a broad range of retention policies on an off-the-shelf system without incurring a significant performance overhead for policy monitoring and enforcement.
inferring semantic query relations from collective user behavior. in this paper we describe how high quality transaction data comprising of online searching, product viewing, and product buying activity of a large online community can be used to infer semantic relationships between queries. we work with a large scale query log consisting of around 115 million queries from ebay. we discuss various techniques to infer semantic relationships among queries and show how the results from these methods can be combined to measure the strength and depict the kinds of relationships. further, we show how this extraction of relations can be used to improve search relevance, related query recommendations, and recovery from null results in an ecommerce context.
effective pattern taxonomy mining in text documents. many data mining techniques have been proposed for mining useful patterns in databases. however, how to effectively utilize discovered patterns is still an open research issue, especially in the domain of text mining. most existing methods adopt term-based approaches. however, they all suffer from the problems of polysemy and synonymy. this paper presents an innovative technique, pattern taxonomy mining, to improve the effectiveness of using discovered patterns for finding useful information. substantial experiments on rcv1 demonstrate that the proposed solution achieves encouraging performance.
mining social networks using heat diffusion processes for marketing candidates selection. social network marketing techniques employ pre-existing social networks to increase brands or products awareness through word-of-mouth promotion. full understanding of social network marketing and the potential candidates that can thus be marketed to certainly offer lucrative opportunities for prospective sellers. due to the complexity of social networks, few models exist to interpret social network marketing realistically. we propose to model social network marketing using heat diffusion processes. this paper presents three diffusion models, along with three algorithms for selecting the best individuals to receive marketing samples. these approaches have the following advantages to best illustrate the properties of real-world social networks: (1) we can plan a marketing strategy sequentially in time since we include a time factor in the simulation of product adoptions; (2) the algorithm of selecting marketing candidates best represents and utilizes the clustering property of real-world social networks; and (3) the model we construct can diffuse both positive and negative comments on products or brands in order to simulate the complicated communications within social networks. our work represents a novel approach to the analysis of social network marketing, and is the first work to propose how to defend against negative comments within social networks. complexity analysis shows our model is also scalable to very large social networks.
structure feature selection for graph classification. with the development of highly efficient graph data collection technology in many application fields, classification of graph data emerges as an important topic in the data mining and machine learning community. towards building highly accurate classification models for graph data, here we present an efficient graph feature selection method. in our method, we use frequent subgraphs as features for graph classification. different from existing methods, we consider the spatial distribution of the subgraph features in the graph data and select those ones that have consistent spatial location. we have applied our feature selection methods to several cheminformatics benchmarks. our method demonstrates a significant improvement of prediction as compared to the state-of-the-art methods.
corpus microsurgery: criteria optimization for medical cross-language ir. automatic subset selection from a parallel corpus significantly cross-lingual information retrieval (clir) performance, in addition to increasing its efficiency. our selection method extracts relevant training data by incorporating additional criteria (i.e. estimated corpus quality, taxonomy projection and size) in addition to lexical-based criteria. the challenge lies in combining these criteria using a meaningful scoring function that can be used for ranking parallel sentence candidates. we choose weighted geometric mean for its soft-and properties, and we optimize criteria weights by wrapping the clir task in an optimization shell. due to the indeterminate nature of the search space convexity properties, we have explored continuous reactive tabu search (crts), a global optimization method. we use a large parallel corpus in the medical domain to examine the effect of adaptation criteria and their combination on clir performance. in our experiments, 100 selected sentences yield 90% of the performance obtained with 5,000 times more in-domain parallel sentences. our optimized criteria weights considerably outperform the uniform distribution baseline, as well as lexical similarity adaptation.
clustered subset selection and its applications on it service metrics. motivated by the enormous amounts of data collected in a large it service provider organization, this paper presents a method for quickly and automatically summarizing and extracting meaningful insights from the data. termed clustered subset selection (css), our method enables program-guided data explorations of high-dimensional data matrices. css combines clustering and subset selection into a coherent and intuitive method for data analysis. in addition to a general framework, we introduce a family of css algorithms with different clustering components such as k-means and close-to-rank-one (cro) clustering, and subset selection components such as best rank-one approximation and rank-revealing qr (rrqr) decomposition. from an empirical perspective, we illustrate that css is achieving significant improvements over existing subset selection methods in terms of approximation errors. compared to existing subset selection techniques, css is also able to provide additional insight about clusters and cluster representatives. finally, we present a case-study of program-guided data explorations using css on a large amount of it service delivery data collection.
error-driven generalist+experts (edge): a multi-stage ensemble framework for text categorization. we introduce a multi-stage ensemble framework, error-driven generalist+expert or edge, for improved classification on large-scale text categorization problems. edge first trains a generalist, capable of classifying under all classes, to deliver a reasonably accurate initial category ranking given an instance. edge then computes a confusion graph for the generalist and allocates the learning resources to train experts on relatively small groups of classes that tend to be systematically confused with one another by the generalist. the experts' votes, when invoked on a given instance, yield a reranking of the classes, thereby correcting the errors of the generalist. our evaluations showcase the improved classification and ranking performance on several large-scale text categorization datasets. edge is in particular efficient when the underlying learners are efficient. our study of confusion graphs is also of independent interest.
an integration strategy for mining product features and opinions. with the development of web 2.0, the web has become an extremely valuable source for mining opinions. in this paper, we study how to automatically mine product features and opinions by integrating multiple review sources. we propose an integration strategy to solve the problem. experiments show that the proposed strategy is effective.
characterizing and predicting community members from evolutionary and heterogeneous networks. mining different types of communities from web data have attracted a lot of research efforts in recent years. however, none of the existing community mining techniques has taken into account both the dynamic as well as heterogeneous nature of web data. in this paper, we propose to characterize and predict community members from the evolution of heterogeneous web data. we first propose a general framework for analyzing the evolution of heterogeneous networks. then, the academic network, which is extracted from 1 million computer science papers, is used as an example to illustrate the framework. finally, two example applications of the academic network are presented. experimental results with a real and very large heterogeneous academic network show that our proposed framework can produce good results in terms of community member recommendation. also, novel knowledge and insights can be gained by analyzing the community evolution pattern.
spam characterization and detection in peer-to-peer file-sharing systems. spam is highly pervasive in p2p file-sharing systems and is difficult to detect automatically before actually downloading a file due to the insufficient and biased description of a file returned to a client as a query result. to alleviate this problem, we first characterize spam and spammers in the p2p file-sharing environment and then describe feature-based techniques for automatically detecting spam in p2p query result sets. experimental results show that the proposed techniques successfully decrease the amount of spam by 9% in the top-200 results and by 92% in the top-20 results.
mining influential attributes that capture class and group contrast behaviour. contrast data mining is a key tool for finding differences between sets of objects, or classes, and contrast patterns are a popular method for discrimination between two classes. however, such patterns can be limited in two primary ways: i) they do not readily allow second order differentiation - i.e. discovering contrasts of contrasts, ii) mining contrast patterns often results in an overwhelming volume of output for the user. to address these limitations, this paper proposes a method which can identify contrast behaviour across both classes and also groups of classes. furthermore, to increase interpretability for the user, it presents a new technique for finding the attributes which represent the key underlying factors behind the contrast behaviour. the associated mining task is computationally challenging and we describe an efficient algorithm to handle it, based on binary decision diagrams. experimental results demonstrate that our technique can efficiently identify and explain contrast behaviour which would be difficult or impossible to isolate using standard techniques.
semi-supervised text categorization by active search. in automated text categorization, given a small number of labeled documents, it is very challenging, if not impossible, to build a reliable classifier that is able to achieve high classification accuracy. to address this problem, a novel web-assisted text categorization framework is proposed in this paper. important keywords are first automatically identified from the available labeled documents to form the queries. search engines are then utilized to retrieve from the web a multitude of relevant documents, which are then exploited by a semi-supervised framework. to our best knowledge, this work is the first study of this kind. extensive experimental study shows the encouraging results of the proposed text categorization framework: using google as the web search engine, the proposed framework is able to reduce the classification error by 30% when compared with the state-of-the-art supervised text categorization method.
multi-scale characterization of social network dynamics in the blogosphere. we have developed a computational framework to characterize social network dynamics in the blogosphere at individual, group and community levels. such characterization could be used by corporations to help drive targeted advertising and to track the moods and sentiments of consumers. we tested our model on a widely read technology blog called engadget. our results show that communities transit between states of high and low entropy, depending on sentiments (positive / negative) about external happenings. we also propose an innovative method to establish the utility of the extracted knowledge, by correlating the mined knowledge with an external time series data (the stock market). our validation results show that the characterized groups exhibit high stock market movement predictability (89%) and removal of 'impactful' groups makes the community less resilient by lowering predictability (26%) and affecting the composition of the groups in the rest of the community.