waashk/dblp huggingface dataset:
asic: algebra-based structural index comparison. structural indices play a significant role in improving the efficiency of xml query evaluation. being able to compare various structural indexing techniques is critical for a dbms to select which indices to support, for the query optimizer to choose an index to use in query evaluation, and for dbas to configure a database application. we present asic, an algebra-based structural index comparison framework that aids users in understanding the ability of different types of structural indices in answering xpath queries which have been characterized using the xpath algebra. asic allows users to select, configure and construct structural indices for comparison, guides users to compare the selected indices by evaluating queries of a particular xpath sub-algebra, and visually displays the index structures, query evaluation plans, and performance results for analysis and comparison.
blogger-centric contextual advertising. this paper addresses the concept of blogger-centric contextual advertising, which refers to the assignment of personal ads to any blog page, chosen in according to bloggers' interests. as blogs become a platform for expressing personal opinions, they naturally contain various kinds of statements, including facts, comments and statements about personal interests, of both a positive and negative nature. to extend the concept behind the long tail theory in contextual advertising, we argue that web bloggers, as the constant visitors of their own blog sites, could be potential consumers who will respond to ads on their own blogs. hence, in this paper, we propose using text mining techniques to discover bloggers' immediate personal interests in order to improve online contextual advertising. the proposed bcca (blogger-centric contextual advertising) framework aims to combine contextual advertising matching with text mining in order to select ads that are related to personal interests as revealed in a blog and rank them according to their relevance. we validate our approach experimentally using a set of data that includes both real ads and actual blog pages. the results indicate that our proposed method could effectively identify those ads that are positively-correlated with a blogger's personal interests.
retrieval experiments using pseudo-desktop collections. desktop search is an important part of personal information management (pim). however, research in this area has been limited by the lack of shareable test collections, making cumulative progress difficult. in this paper, we define desktop search as a semi-structured document retrieval problem and introduce a methodology to automatically build a reusable collection (the pseudo-desktop) that has many of the same properties as a real desktop collection. we then present a comprehensive evaluation of retrieval methods for semi-structured document retrieval on several pseudo-desktop collections and the trec enterprise collection. our results show that a probabilistic retrieval model using the mapping relation between a query term and a document field (prm-s) has the best performance in collections with more structure, such as email, and that the query-likelihood language model is better for other document types. we further analyze the observed differences using generated queries and suggest ways to improve prm-s, which makes the performance gains more significant and consistent.
injecting purpose and trust into data anonymisation. most existing works of data anonymisation target at the optimization of the anonymisation metrics to balance the data utility and privacy, whereas they ignore the effects of a requester's trust level and application purposes during the data anonymisation. our aim of this paper is to propose a much finer level anonymisation scheme with regard to the data requester's trust value and specific application purpose. we prioritize the attributes for anonymisation based on how important and critical they are related to the specified application purposes and propose a trust evaluation strategy to quantify the data requester's reliability, and further build the projection between the trust value and the degree of data anonymiztion, which intends to determine to what extent the data should be anonymized. the decomposition algorithm is developed to find the desired anonymous solution, which guarantees the uniqueness and correctness.
a graphical browser for xml schema documents. recently, tools for browsing xml schema documents have become popular, but they cannot support advanced browsing functionalities. we have implemented a new graphical schema browser which provides traversals not only along a composition hierarchy but also along a type hierarchy defined in the documents. in this demonstration, we show how users can quickly and easily understand the semantic structures by freely navigating to any datatypes or elements located at any position in the hierarchies.
efficient record-level wrapper induction. web information is often presented in the form of record, e.g., a product record on a shopping website or a personal profile on a social utility website. given a host webpage and related information needs, how to identify relevant records as well as their internal semantic structures is critical to many online information systems. wrapper induction is one of the most effective methods for such tasks. however, most traditional wrapper techniques have issues dealing with web records since they are designed to extract information from a page, not a record. we propose a record-level wrapper system. in our system, we use a novel ``broom'' structure to represent both records and generated wrappers. with such representation, our system is able to effectively extract records and identify their internal semantics at the same time. we test our system on 16 real-life websites from four different domains. experimental results demonstrate 99\% extraction accuracy in terms of f1-value.
olap with udfs in digital libraries. queries on digital libraries generally involve the retrieval of specific documents, but most techniques lack the ability to efficiently explore these collections. the integration of olap techniques with digital libraries allows users to navigate throughout these collections on multiple levels. in order to accomplish this, we propose the creation of olap networks, a complex data structure that contains summarized representations of the original collection of metadata to enrich traditional retrievals and allow the users to quickly explore the collection. we developed a system that enables olap-based exploration on the metadata of digital libraries through the use of a combination of efficient udfs and optimized sql queries. in addition, we also incorporated visualization methods into our system to allow fast navigation and exploration.
low-cost management of inverted files for online full-text search. in dynamic environments with frequent content updates, we require online full-text search that scales to large data collections and achieves low search latency. several recent methods that support fast incremental indexing of documents typically keep on disk multiple partial index structures that they continuously update as new documents are added. however, spreading indexing information across multiple locations on disk tends to considerably decrease the search responsiveness of the system. in the present paper, we take a fresh look at the problem of online full-text search with consideration of the architectural features of modern systems. selective range flush is a greedy method that we introduce to manage the index in the system by using fixed-size blocks to organize the data on disk and dynamically keep low the cost of data transfer between memory and disk. as we experimentally demonstrate with the proteus prototype implementation that we developed, we retrieve indexing information at latency that matches the lowest achieved by existing methods. additionally, we reduce the total building cost by 30% in comparison to methods with similar retrieval time.
generating synopses for document-element search. scientists often search for document-elements like tables, figures, or algorithm pseudo-codes. domain scientists and researchers report important data, results and algorithms using these document-elements; readers want to compare the reported results with their findings. some document-element search engines have been proposed (especially to search for tables and figures) to make this task easier. while searching for document-elements today, the end-user is presented with the caption of the document-element and a sentence in the document text that refers to the document-element. oftentimes, the caption and the reference text do not contain enough information to interpret the document-element. in this paper, we present the first set of methods to extract this useful information (synopsis) related to document-elements automatically. we also investigate the problem of choosing the optimum synopsis-size that strikes a balance between information content and size of the generated synopses.
reducing the risk of query expansion via robust constrained optimization. we introduce a new theoretical derivation, evaluation methods, and extensive empirical analysis for an automatic query expansion framework in which model estimation is cast as a robust constrained optimization problem. this framework provides a powerful method for modeling and solving complex expansion problems, by allowing multiple sources of domain knowledge or evidence to be encoded as simultaneous optimization constraints. our robust optimization approach provides a clean theoretical way to model not only expansion benefit, but also expansion risk, by optimizing over uncertainty sets for the data. in addition, we introduce risk-reward curves to visualize expansion algorithm performance and analyze parameter sensitivity. we show that a robust approach significantly reduces the number and magnitude of expansion failures for a strong baseline algorithm, with no loss in average gain. our approach is implemented as a highly efficient post-processing step that assumes little about the baseline expansion method used as input, making it easy to apply to existing expansion methods. we provide analysis showing that this approach is a natural and effective way to do selective expansion, automatically reducing or avoiding expansion in risky scenarios, and successfully attenuating noise in poor baseline methods.
dynamic in-page logging for flash-aware b-tree index. 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.
exploiting query views for static index pruning in web search engines. we propose incorporating query views in a number of static pruning strategies, namely term-centric, document-centric and access-based approaches. these query-view based strategies considerably outperform their counterparts for both disjunctive and conjunctive query processing in web search engines.
intention-focused active reranking for image object retrieval. we consider the problem of ranking refinement for image object retrieval, whose goal is to improve an existing ranking function by a small number of labeled instances. to retrieve the relevant image object, one state-of-the-art approach is to use the relevance feedback: it first ranks the images in database based on a given ranking function (i.e., base ranker), and then rerank the initial result by further introducing user's feedback information. the key challenge of combining the information from the base ranker and user's feedback comes from the fact that the base ranker tends to give an imperfect result and the information obtained from user's feedback tends to be very noisy. this paper describes an intention-focused active reranking, an approach for automatically finding the right information to re-estimate the query model. three novel strategies are proposed to boost the performance of the base ranker: (1) an active selection criterion, which obtains a small number of feedback images that are the most informative to the base ranker for user labeling; (2) the user intention verification, which captures the user's intention in object level to alleviate the query drift problem; (3) a discriminative query model re-estimation, which augments the generative approach with a model of the discriminative information conveyed by positive and negative feedback information. experiments on a real world data set demonstrate the effectiveness of the proposed approach and furthermore it significantly outperforms the baseline visual bag-of-words retrieval.
subspace maximum margin clustering. in text mining, we are often confronted with very high dimensional data. clustering with high dimensional data is a challenging problem due to the curse of dimensionality. in this paper, to address this problem, we propose an subspace maximum margin clustering (smmc) method, which performs dimensionality reduction and maximum margin clustering simultaneously within a unified framework. we aim to learn a subspace, in which we try to find a cluster assignment of the data points, together with a hyperplane classifier, such that the resultant margin is maximized among all possible cluster assignments and all possible subspaces. the original problem is transformed from learning the subspace to learning a positive semi-definite matrix, in order to avoid tuning the dimensionality of the subspace. the transformed problem can be solved efficiently via cutting plane technique and constrained concave-convex procedure (cccp). since the sub-problem in each iteration of cccp is joint convex, alternating minimization is adopted to obtain the global optimum. experiments on benchmark data sets illustrate that the proposed method outperforms the state of the art clustering methods as well as many dimensionality reduction based clustering approaches.
combining labeled and unlabeled data with word-class distribution learning. we describe a novel simple and highly scalable semi-supervised method called word-class distribution learning (wcdl), and apply it task of information extraction (ie) by utilizing unlabeled sentences to improve supervised classification methods. wcdl iteratively builds class label distributions for each word in the dictionary by averaging predicted labels over all cases in the unlabeled corpus, and re-training a base classifier adding these distributions as word features. in contrast, traditional self-training or co-training methods self-labeled examples (rather than features) which can degrade performance due to incestuous learning bias. wcdl exhibits robust behavior, and has no difficult parameters to tune. we applied our method on german and english name entity recognition (ner) tasks. wcdl shows improvements over self-training, multi-task semi-supervision or supervision alone, in particular yielding a state-of-the art 75.72 f1 score on the german ner task.
learning to rank with a novel kernel perceptron method. while conventional ranking algorithms, such as the pagerank, rely on the web structure to decide the relevancy of a web page, learning to rank seeks a function capable of ordering a set of instances using a supervised learning approach. learning to rank has gained increasing popularity in information retrieval and machine learning communities. in this paper, we propose a novel nonlinear perceptron method for rank learning. the proposed method is an online algorithm and simple to implement. it introduces a kernel function to map the original feature space into a nonlinear space and employs a perceptron method to minimize the ranking error by avoiding converging to a solution near the decision boundary and alleviating the effect of outliers in the training dataset. furthermore, unlike existing approaches such as ranksvm and rankboost, the proposed method is scalable to large datasets for online learning. experimental results on benchmark corpora show that our approach is more efficient and achieves higher or comparable accuracies in instance ranking than state of the art methods such as frank, ranksvm and rankboost.
supporting ranking pattern-based aggregate queries in sequence data cubes. sequence data processing has been studied extensively in the literature. in recent years, the warehousing and online-analytical processing (olap) of archived sequence data have received growing attentions. in particular, the concept of sequence olap is recently proposed with the objective of evaluating various kinds of so-called pattern-based aggregate (pba) queries so that various kinds of data analytical tasks on sequence data can be carried out efficiently. this paper studies the evaluation of ranking pba queries, which rank the results of pba queries and return only the top-ranked ones to users. we discuss how ranking pba queries drastically improve the usability of s-olap systems and present techniques that can evaluate various kinds of ranking pba queries efficiently.
exploring path query results through relevance feedback. feedback driven data exploration schemes have been implemented for non-structured data (such as text) and document-centric xml collections where formulating precise queries is often impossible. in this paper, we study the problem of enabling exploratory access, through ranking, to data-centric xml. given a path query and a set of results identified by the system to this query over the data, we consider feedback which captures the user's preference for some features over the others. the feedback can be "positive" or "negative". to deal with feedback, we develop a probabilistic feature significance measure and describe how to use this for ranking results in the presence of dependencies between the path features. we bring together these techniques in axp, a system for adaptive and exploratory path retrieval. the experimental results show the effectiveness of the proposed techniques.
modeling context-dependent information. object properties are often based on their contexts, and contexts can be nested to form complex context-dependent information. existing data models cannot naturally and directly represent such context-dependent information. in this paper, we propose a novel mechanism called context constructor in an object-oriented framework to solve this problem.
advanced metasearch engines. a metasearch engine is a system, which is connected to different search engines. in response to a user query, it invokes suitable search engines for the query, merges the information returned by these search engines and output the merged result. there are two types of metasearch engines: one type for unstructured data (mostly text) and the other for structured data. in comparison to a text search engine, a metasearch engine can have a higher coverage of the web and can have more timely information. a metasearch engine for structured data facilitates comparison shopping and services and is convenient to use. in this talk, we discuss the problems and their potential solutions. in addition, challenges and unsolved problems are sketched.
diverging patterns: discovering significant frequency change dissimilarities in large databases. in this paper, we present a framework for mining diverging patterns, a new type of contrast patterns whose frequency changes significantly differently in two data sets, e.g., it changes from a relatively low to a relatively high value in one dataset, but from high to low in the other. in this framework, a measure called diverging ratio is defined and used to discover diverging patterns. we use a four-dimensional vector to represent a pattern, and define the pattern's diverging ratio based on the angular difference between its vectors in two datasets. an algorithm is proposed to mine diverging patterns from a pair of datasets, which makes use of a standard frequent pattern mining algorithm to compute vector components efficiently. we demonstrate the effectiveness of our approach on real-world datasets, showing that the method can reveal novel knowledge from large databases.
a framework for safely publishing communication traces. a communication trace is a detailed record of the communication between two entities. communication traces are vital for research in computer networks and study of network protocols in various domains, but their release is severely constrained by privacy and security concerns. in this paper, we propose a framework in which a trace owner can match an anonymizing transformation with the requirements of analysts. the trace owner can release multiple transformed traces, each customized to an analyst's needs, or a single transformation satisfying all requirements. the framework enables formal reasoning about anonymization policies, for example to verify that a given trace has utility for the analyst, or to obtain the most secure anonymization for the desired level of utility. because communication traces are typically very large, we also provide techniques that allow efficient application of transformations using relational database systems.
practical lessons of data mining at yahoo! the usage of data in many commercial applications has been growing at an unprecedented pace in the last decade. while successful data mining efforts lead to major business advances, there were also numerous, less publicized efforts that for one or another reason failed. in this paper, we discuss practical lessons based on years of our data mining experiences at yahoo! and offer insights into how to drive the data mining effort to success in a business environment. we use two significant yahoo's applications as illustrative examples: shopping categorization and behavioral targeting; and reflect on four success factors: methodology, data, infrastructure, and people.
dissemination of heterogeneous xml data in publish/subscibe systems. the publish-subscribe paradigm is an effective approach for data publishers to asynchronously disseminate relevant data to a large number of data subscribers. a lot of recent research has focused on extending this paradigm to support content-based delivery of xml data using more expressive xml-based subscription specifications that allow constraints on both data contents as well as structure. however, due to the heterogeneous data schemas used by different data publishers even for data in the same domain, an important challenge is how to efficiently and effectively disseminate relevant data to subscribers whose subscriptions might be specified based on schemas that are different from those used by the data publishers. in this paper, we examine the options to resolve this schema heterogeneity problem in xml data dissemination, and propose a novel paradigm that is based on data rewriting. our experimental results demonstrate the effectiveness of the data rewriting paradigm and identifies the tradeoffs of the various approaches.