waashk/dblp huggingface dataset:
an object-oriented extension of xml for autonomous web applications. while the idea of extending xml to include object-oriented features has been gaining popularity in general, the potential of inheritance in document design has not been well recognized in contemporary research. in this paper we demonstrate that xml with dynamic inheritance aids better document designs and decreased management overheads and support increased autonomy. as an extended application, we point out that dynamic inheritance also helps effective automated web portal and ontology designs.we present an object-oriented extension to the language of xml to include dynamic inheritance and describe a middle layer that implements our system. we explain our system with several practical examples.
towards a visual query interface for phylogenetic databases. querying and visualization of phylogenetic databases remain a great challenge due to their complex tree type semi structured nature. naturally, successful phylogenetic databases such as the tree of life database at the university of arizona are implemented as web documents in html. while web implementation of such databases facilitate the representation, and in part, visualization of their contents, querying remains an issue. the interoperability of web-based phylogenetic databases with other similar databases such as treebase and rdb which are implemented using traditional database management systems, has not been possible due to the impedance mismatch between the underlying query and data representation framework. in this paper, we present a novel approach to phylogentic database management using existing database technologies without compromising potential opportunities for visualization and interoperability. we present a web-based tool for the creation, querying and visualization of phylogenetic databases. we demonstrate the functional capabilities and strengths of our system by recreating the tree of life database in our system and performing queries that are not possible in the original tree of life database.
an effective mechanism for index update in structured documents. indexing and retrieval of structured documents have been drawing attention increasingly since they enable to retrieve and access a certain part of a document easily. so far, several methods have been proposed in the setting that documents are rarely changed. these can be applied for the books or journals possessed in libraries, but hardly work for the documents that are subject to change frequently in the business domain. this paper aims at enabling incremental update of indices whenever parts of documents are changed. for this, it employs the index-organized table that has been developed for the full-text retrieval in oracle. it creates several index-organized tables that are essential in implementing the bottom up scheme strategy, which has been developed for manipulating structured documents efficiently.along with an experiment, the technique presented here does not add much index overhead to the original one taken to the index organized table. in addition, the updates of indices are performed quickly as soon as parts of documents are changed.
parallelizing the buckshot algorithm for efficient document clustering. we present a parallel implementation of the buckshot document clustering algorithm. we demonstrate that this parallel approach is highly efficient both in terms of load balancing and minimization of communication. in a series of experiments using the 2gb of sgml data from trec disks 4 and 5, our parallel approach was shown to be scalable in terms of processors efficiently used and the number of clusters created.
finding similar questions in large question and answer archives. there has recently been a significant increase in the number of community-based question and answer services on the web where people answer other peoples' questions. these services rapidly build up large archives of questions and answers, and these archives are a valuable linguistic resource. one of the major tasks in a question and answer service is to find questions in the archive that a semantically similar to a user's question. this enables high quality answers from the archive to be retrieved and removes the time lag associated with a community-based system. in this paper, we discuss methods for question retrieval that are based on using the similarity between answers in the archive to estimate probabilities for a translation-based retrieval model. we show that with this model it is possible to find semantically similar questions with relatively little word overlap.
induction of integrated view for xml data with heterogeneous dtds. this paper proposes a novel approach to integrating heterogeneous xml dtds. with this approach, an information agent can be easily extended to integrate heterogeneous xml-based contents and perform federated search. based on a tree grammar inference technique, this approach derives an integrated view of xml dtds in an information integration framework. the derivation takes advantages of naming and structural similarities among dtds in similar domains. the complete approach consists of three main steps. (1) dtd clustering clusters dtds in similar domains into classes. (2) schema learning applies a tree grammar inference technique to generate a set of tree grammar rules from the dtds in a class from the previous step. (3) minimization optimizes the rules generated in the previous step and transforms them into an integrated view. we have implemented the proposed approach into a system called deep and tested the system on artificial and real domains. the experimental results reveal that this system can effectively and efficiently integrate radically different dtds.
queueing analysis of relational operators for continuous data streams. currently, stream data processing is an active area of research, which includes everything from algorithms and architectures for stream processing to modelling, and analysis of various components of a stream processing system. in this paper, we present an analysis of relational operators used for stream processing using queueing theory and study behaviors of streaming data in a query processing system. our approach enables us to compute the fundamental performance metrics of relational operators ---select, project, and join over data streams. furthermore, this approach establishes a way to find the probability distribution functions of both the number of tuples and the waiting time of tuples in the system. finally, we designed and implemented a number of experiments to validate the accuracy and effectiveness of our analysis.
re-evaluating indexing schemes for nested objects. performance is a major issue in the acceptance of object-oriented database management systems (oodbms). the nested index and path index schemes have been criticized for their heavy costs and poor handling of update operations. this paper re-evaluates three index schemes (nested index, path index, and multi-index) applicable to queries on nested attributes. among these, we found that a multi-index scheme is best supported in the object-oriented or extended relational dbms environment. multi-index schemes not only provide a better balance between retrieval and update costs than do the nested or path indices, but they also scale well for update when the number of indices increases. in this paper, we propose a multi-index design that reuses the single-table index structures already present in a dbms. our performance study extends previous models by permitting attributes to be multi-valued as well as single-valued. we also suggest that a combination of nested index and multi-index schemes offers a feasible solution to the support of queries on nested objects.
accurately extracting coherent relevant passages using hidden markov models. in this paper, we present a principled method for accurately extracting coherent relevant passages of variable lengths using hmms. we show that with appropriate parameter estimation, the hmm method outperforms a number of strong baseline methods on two data sets.
retrieving answers from frequently asked questions pages on the web. we address the task of answering natural language questions by using the large number of frequently asked questions (faq) pages available on the web. the task involves three steps: (1) fetching faq pages from the web; (2) automatic extraction of question/answer (q/a) pairs from the collected pages; and (3) answering users' questions by retrieving appropriate q/a pairs. we discuss our solutions for each of the three tasks, and give detailed evaluation results on a collected corpus of about 3.6gb of text data (293k pages, 2.8m q/a pairs), with real users' questions sampled from a web search engine log. specifically, we propose simple but effective methods for q/a extraction and investigate task-specific retrieval models for answering questions. our best model finds answers for 36% of the test questions in the top 20 results. our overall conclusion is that faq pages on the web provide an excellent resource for addressing real users' information needs in a highly focused manner.
dynamically maintaining frequent items over a data stream. it is challenge to maintain frequent items over a data stream, with a small bounded memory, in a dynamic environment where both insertion/deletion of items are allowed. in this paper, we propose a new novel algorithm, called hcount, which can handle both insertion and deletion of items with a much less memory space than the best reported algorithm. our algorithm is also superior in terms of precision, recall and processing time. in addition, our approach does not request the preknowledge on the size of range for a data stream, and can handle range extension dynamically. given a little modification, algorithm hcount can be improved to hcount*, which even owns significantly better performance than before.
collaborative filtering with decoupled models for preferences and ratings. in this paper, we describe a new model for collaborative filtering. the motivation of this work comes from the fact that two users with very similar preferences on items may have very different rating schemes. for example, one user may tend to assign a higher rating to all items than another user. unlike previous models of collaborative filtering, which determine the similarity between two users only based on their rating performance, our model treats the user's preferences on items separately from the user's rating scheme. more specifically, for each user, we build two separate models: a preference model capturing which items are favored by the user and a rating model capturing how the user would rate an item given the preference information. the similarity of two users is computed based on the underlying preference model, instead of the surface ratings. we compare the new model with several representative previous approaches on two data sets. experiment results show that the new model outperforms all the previous approaches that are tested consistently on both data sets.
localized signature table: fast similarity search on transaction data. recently, techniques for supporting efficient similarity search over huge transaction datasets have emerged as an important research area. several indexing schemes have been proposed towards this direction. typically, these schemes provide a tradeoff between searching efficiency and indexing overhead in terms of space. in this paper, we propose a novel indexing scheme for similarity search on transaction data. based on well-studied clustering techniques, we develop a construction algorithm for the proposed index and a branch-and-bound searching strategy for answering similarity search. unlike previous techniques, our indexing scheme exhibits high search efficiency and low space requirements by trading-off the pre-computation time. this behavior is ideal for applications with low update but high read volume <i>e.g.</i>, data warehousing, collaborative filtering, <i>etc.</i>). moreover, our experimental results illustrate that our method is robust to the varying characteristics of the datasets.
linguistic instruments and qualitative reasoning for schema integration. two major problems in schema integration are to identify correspondences between different conceptual schemas and to verify that the proposed correspondences are consistent with the semantics of the schemas. we propose a heuristic method, based on the use of galois lattices, for identifying schema correspondences. we show how the results of this method can be checked for correctness by introducing a number of necessary conditions for schema mergeability. these conditions are formulated in the context of a semantically rich modelling formalism, the distinguishing feature of which is the use of case grammar.
extending complex ad-hoc olap. large scale data analysis and mining activities require sophisticated information extraction queries. many queries require complex aggregation, and many of these aggregates are non-distributive. conventional solutions to this problem involve defining user defined aggregate functions (udafs). however, the use of udafs entails several problems. defining a new udaf can be a significant burden for the user, and optimizing queries involving udafs is difficult because of the &ldquo;black box&rdquo; nature of the udaf.in this paper, we present a method for expressing nested aggregates in a declarative way. a nested aggregate, which is a rollup of another aggregated value, expresses a wide range of useful non-distributive aggregation. for example, most frequent type aggregation can be naturally expressed using nested aggregation, e.g. &ldquo;for each product, report its total sales during the month with the largest total sales of the product&rdquo;. by expressing compex aggregates declaratively, we relieve the user of the burden of defining udafs, and allow the evalution of the complex aggregates to be optimized.we use the extended multi-feature (emf) syntax as the basis for expressing nested aggregation. an advantage of this approach is that emf sql can already express a wide range of complex aggregation in a succinct way, and emf sql is easily optimized into efficient query plans. we show that nested aggregation queries can be evaluated efficiently by using a small extension to the emf sql query evaluation algorithm. a side effect of this extension is to extend emf sql to permit complex aggregation of data from multiple sources.
keeping found things found on the web. this paper describes the results of an observational study into the methods people use to manage web information for re-use. people observed in our study used a diversity of methods and associated tools. for example, several participants emailed web addresses (urls) along with comments to themselves and to others. other methods observed included printing out web pages, saving web pages to the hard drive, pasting the address for a web page into a document and pasting the address into a personal web site. ironically, two web browser tools that have been explicitly developed to help users track web information - the bookmarking tool and the history list - were not widely used by participants in this study. a functional analysis helps to explain the observed diversity of methods. methods vary widely in the functions they provide. for example, a web address pasted into a self-addressed email can provide an important reminding function together with a context of relevance: the email arrives in an inbox which is checked at regular intervals and the email can include a few lines of text that explain the url's relevance and the actions to be taken. on the other hand, for most users in the study, the bookmarking tool ("favorites" or "bookmarks" depending on the browser) provided neither a reminding function nor a context of relevance. the functional analysis can help to assess the likely success of various tools, current and proposed.
serf: integrating human recommendations with search. today's university library has many digitally accessible resources, both indexes to content and considerable original content. using off-the-shelf search technology provides a single point of access into library resources, but we have found that such full-text indexing technology is not entirely satisfactory for library searching. in response to this, we report initial usage results from a prototype of an entirely new type of search engine - the system for electronic recommendation filtering (serf) - that we have designed and deployed for the oregon state university (osu) libraries. serf encourages users to enter longer and more informative queries, and collects ratings from users as to whether search results meet their information need or not. these ratings are used to make recommendations to later users with similar needs. over time, serf learns from the users what documents are valuable for what information needs. in this paper, we focus on understanding whether such recommendations can increase other users' search efficiency and effectiveness in library website searching. based on examination of three months of usage as an alternative search interface available to all users of the oregon state university libraries website (http://osulibrary.oregonstate.edu/), we found strong evidence that the recommendations with human evaluation could increase the efficiency as well as effectiveness of the library website search process. those users who received recommendations needed to examine fewer results, and recommended documents were rated much higher than documents returned by a traditional search engine.
kalchas: a dynamic xml search engine. this paper outlines the system architecture and the core data structures of kalchas, a fulltext search engine for xml data with emphasis on dynamic indexing, and identifies features worth demonstrating. the concept of dynamic index implies that the aim is to re ect the creation of, deletion of, and updates to relevant files in the search index as early as possible. this is achieved by a number of techniques, including ideas drawn from partitioned b-trees and inverted indices. the actual ranked retrieval of document is implemented with xml-specific query operators for lowest common ancestor queries.a live demonstration will discuss kalchas' behaviour in typical use cases, such as interactive editing sessions and bulk loading large amounts of static files as well as querying the contents of the indexed files; it tries to clarify both the short-comings and the advantages of the method.
a local search mechanism for peer-to-peer networks. one important problem in peer-to-peer (p2p) networks is searching and retrieving the correct information. however, existing searching mechanisms in pure peer-to-peer networks are inefficient due to the decentralized nature of such networks. we propose two mechanisms for information retrieval in pure peer-to-peer networks. the first, the modified breadth-first search (bfs) mechanism, is an extension of the current gnuttela protocol, allows searching with keywords, and is designed to minimize the number of messages that are needed to search the network. the second, the intelligent search mechanism, uses the past behavior of the p2p network to further improve the scalability of the search procedure. in this algorithm, each peer autonomously decides which of its peers are most likely to answer a given query. the algorithm is entirely distributed, and therefore scales well with the size of the network. we implemented our mechanisms as middleware platforms. to show the advantages of our mechanisms we present experimental results using the middleware implementation.
web-centric language models. we investigate language models for informational and navigational web search. retrieval on the web is a task that differs substantially from ordinary ad hoc retrieval. we perform an analysis of prior probability of relevance for a wide range of non-content features, shedding further light on the importance of non-content features for web retrieval. this directly explains the success or failure of various techniques, e.g., why the link topology is particularly helpful to single out important sites. language models can naturally incorporate multiple document representations, as well as non-content information. for the former, we employ mixture language models based on document full-text, incoming anchor-text, and document titles. for the latter, we study a range of priors based on document length, url structure, and link topology. we look at three types of topics--distillation, home page, and named page--as well as for a mixed query set. we find that the mixture models lead to considerable improvement of retrieval effectiveness for all topic types. the web-centric priors generally lead to further improvement of retrieval effectiveness.
structured queries in xml retrieval. document-centric xml is a mixture of text and structure. with the increased availability of document-centric xml content comes a need for query facilities in which both structural constraints and constraints on the content of the documents can be expressed. how does the expressiveness of languages for querying xml documents help users to express their information needs? we address this question from both an experimental and a theoretical point of view. our experimental analysis compares a structure-ignorant with a structure-aware retrieval approach using the test-suite of the 2004 edition of the inex xml retrieval evaluation initiative. theoretically, we create mathematical models of users' knowledge of a set of documents and define query languages which exactly fit these models. one of these languages corresponds to an xml version of fielded search, the other to the inex query language. our main findings are: first, while structure is used in varying degrees of complexity, over half of the queries can be expressed in a fielded-search like format which does not use the hierarchical structure of the documents. second, structure is used as a search hint, and not a strict requirement, when judged against the underlying information need. third, the use of structure in queries functions as a precision enhancing device.
fast webpage classification using url features. we demonstrate the usefulness of the uniform resource locator (url) alone in performing web page classification. this approach is faster than typical web page classification, as the pages do not have to be fetched and analyzed. our approach segments the url into meaningful chunks and adds component, sequential and orthographic features to model salient patterns. the resulting features are used in supervised maximum entropy modeling. we analyze our approach's effectiveness on two standardized domains. our results show that in certain scenarios, url-based methods approach the performance of current state-of-the-art full-text and link-based methods.
a method of geographical name extraction from japanese text for thematic geographical search. a text retrieval method called the thematic geographical search method has been developed and applied to a japanese encyclopedia called the world encyclop&aelig;dia. in this method, the user specifies a search theme using free words, then obtains a sorted list of excerpts and hyperlinks to encyclopedia sentences that contain geographical names. using this list, the user can also open maps that indicate the locations of the names. to generate an index of names for this searching, a method of extracting geographical names has been developed. in this method, geographical names are extracted, matched to names in a geographical name database, and identified. geographical names, however, often have several types of ambiguities. ambiguities are resolved by using non-local context analysis, which uses a stack and several other techniques. as a result, the precision of extracted names is more than 96% on average. this method depends on features of the japanese language, but the strategy and most of the techniques can be applied to texts in english or other languages.