waashk/dblp huggingface dataset:
establishing value mappings using statistical models and user feedback. in this paper, we present a "value mapping" algorithm that does not rely on syntactic similarity or semantic interpretation of the values. the algorithm first constructs a statistical model (e.g., co-occurrence frequency or entropy vector) that captures the unique characteristics of values and their co-occurrence. it then finds the matching values by computing the distances between the models while refining the models using user feedback through iterations. our experimental results suggest that our approach successfully establishes value mappings even in the presence of opaque data values and thus can be a useful addition to the existing data integration techniques.
regularizing translation models for better automatic image annotation. the goal of automatic image annotation is to automatically generate annotations for images to describe their content. in the past, statistical machine translation models have been successfully applied to automatic image annotation task [8]. it views the process of annotating images as a process of translating the content from a 'visual language' to textual words. one problem with the existing translation models is that common words are usually associated with too many different image regions. as a result, uncommon words have little chance to be used for annotating images. uncommon words are important for automatic image annotation because they are often used in the queries. in this paper, we propose two modified translation models for automatic image annotation, namely the normalized translation model and the regularized translation model, that specifically address the problem of common annotated words. the basic idea is to raise the number of blobs that are associated with uncommon words. the normalized translation model realizes this by scaling translation probabilities of different words with different factors. the same goal is achieved in the regularized translation model through the introduction of a special dirichlet prior. empirical study with the corel dataset has shown that both two modified translation models outperform the original translation model and several existing approaches for automatic image annotation substantially.
indexing field values in field oriented systems: interval quadtree. with the extension of spatial database applications, field oriented systems emerge as an important research issue in order to deal with continuous natural phenomena during the last years. it however has a large volume of data and efficient indexing methods for field data are necessary to overcome the performance obstacle. in special, we introduce indexing methods for field value queries (i.e. searching some regions where the temperature is more 20 degrees). we introduce the concept of subfield and show how we make use of this concept to index field values in field oriented systems. we present two implementation methods based on quadtree space subdivision. we modify traditional linear quadtree implementation method for field value query processing using subfields. we analyze the performance of our methods. experimentation with real terrain data shows that proposed indexing methods improve the query processing time of field value queries in comparison with the case of no indexing method.
entropy-based link analysis for mining web informative structures. in this paper, we study the problem of mining the informative structure of a news web site which consists of thousands of hyperlinked documents. we define the informative structure of a news web site as a set of index pages (or referred to as toc, i.e., table of contents, pages) and a set of article pages linked by toc pages through informative links. it is noted that the hyperlink induced topics search (hits) algorithm has been employed to provide a solution to analyzing authorities and hubs of pages. however, most of the content sites tend to contain some extra hyperlinks, such as navigation panels, advertisements and banners, so as to increase the add-on values of their web pages. therefore, due to the structure induced by these extra hyperlinks, hits is found to be insufficient to provide a good precision in solving the problem. to remedy this, we develop an algorithm to utilize entropy-based link analysis on mining web informative structures. this algorithm is referred to as lamis. the key idea of lamis is to utilize information entropy for representing the knowledge that corresponds to the amount of information in a link or a page in the link analysis. experiments on several real news web sites show that the precision and the recall of lamis are much superior to those obtained by heuristic methods and conventional ink analysis methods.
updates and view maintenance in soft real-time database systems. a database system contains base data items which record and model a physical, real world environment. for better decision support, base data items are summarized and correlated to derive views. these base data and views are accessed by application transactions to generate the ultimate actions taken by the system. as the environment changes, updates are applied to the base data, which subsequently trigger view recomputations. there are thus three types of activities: base data update, view recomputation, and transaction execution. in a real-time system, two timing constrains need to be enforced. we require transactions meet their deadlines (transaction timeliness) and read fresh data (data timeliness). in this paper we define the concept of absolute and relative temporal consistency from the perspective of transactions. we address the important issue of transaction scheduling among the three types of activities such that the two timing requirements can be met. we also discuss how a real-time database system should be designed to enforce different levels of temporal consistency.
user assisted text classification and knowledge management. while there are many aspects to managing corporate knowledge, one key issue is how to organize corporate documents into categories of interest. in this paper, we focus on using user assisted text classification in conjunction with a web portal, multiple document management systems and an ontology, to provide a powerful solution for organizing information about a company's technology. we propose a system that interacts with an author using an automatic text classifier to suggest controlled keywords to be used as metadata. the proposed approach does not require professional librarians or that the end users have extensive training. the use of a controlled vocabulary allows for a more consistent description of corporate documents, and promotes easier access by people across the company. it is easier to find similar documents which use different nomenclature. finally, the interactive nature of the system results in a more correct and precise description of each document than a fully automatic system would.
a storage system for scalable knowledge representation. twenty years of ai research in knowledge representation has produced frame knowledge representation systems (frss) that incorporate a number of important advances. however, frss lack two important capabilities that prevent them from scaling up to realistic applications: they cannot provide high-speed access to large knowledge bases (kbs), and they do not support shared, concurrent kb access by multiple users. our research investigates the hypothesis that one can employ an existing database management system (dbms) as a storage subsystem for an frs, to provide high-speed access to large, shared kbs. we describe the design and implementation of a general storage system that incrementally loads referenced frames from a dbms, and saves modified frames back to the dbms, for two different frss: loom and theo. we also present experimental results showing that the performance of our prototype storage subsystem exceeds that of flat files for simulated applications that reference or update up to one third of the frames from a large loom kb.
evaluation of item-based top-n recommendation algorithms. the explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of n items that will be of interest to a certain user. user-based collaborative filtering is the most successful technology for building recommender systems to date, and is extensively used in many commercial recommender systems. unfortunately, the computational complexity of these methods grows linearly with the number of customers that in typical commercial applications can grow to be several millions. to address these scalability concerns item-based recommendation techniques have been developed that analyze the user-item matrix to identify relations between the different items, and use these relations to compute the list of recommendations.in this paper we present one such class of item-based recommendation algorithms that first determine the similarities between the various items and then used them to identify the set of items to be recommended. the key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. our experimental evaluation on five different datasets show that the proposed item-based algorithms are up to 28 times faster than the traditional user-neighborhood based recommender systems and provide recommendations whose quality is up to 27% better.
semantics-based information brokering. the rapid advances in computer and communication technologies, and their merger, is leading to a global information market place. it will consist of federations of very large number of information systems that will cooperate to varying extents to support the users' information needs. we discuss an approach to information brokering in the above environment. we discuss two of its tasks: information resource discovery, which identifies relevant information sources for a given query, and query processing, which involves the generation of appropriate mapping from relevant but structurally heterogeneous objects. query processing consists of information focusing and information correlation.our approach is based on: semantic proximity, which represents semantic similarities based on the context of comparison, and schema correspondences which are used to represent structural mappings and are associated with the context. the context of comparison of the two objects is the primary vehicle to represent the semantics for determining semantic proximity. specifically, we use a partial context representation to capture the semantics in terms of the assumptions in the intended use of the objects and the intended meaning of the user query. information focusing is supported by subsequent context comparison. the same mechanism can be used to support information resource discovery. context comparison leads to changes in schema correspondences that are used to support information correlation.
xel: extended ephemeral logging for log storage management. extended ephemeral logging (xel) is a more general variation of the ephemeral logging (el) technique for managing a log of database activity on disk; it does not require a timestamp to be maintained with each object in the database. xel does not require periodic checkpoints and does not abort lengthy transactions as frequently as traditional firewall logging for the same amount of disk space. therefore, it is well suited for concurrent databases and applications which have a wide distribution of transaction lifetimes.simulation results indicate that xel can offer significant savings in disk space, at the expense of slightly higher bandwidth for logging and more main memory. the reduced size of the log permits much faster recovery after a crash as well as cost savings.
features of documents relevant to task- and fact-oriented questions. we describe results from an ongoing project that considers question types and document features and their relationship to retrieval techniques. we examine eight document features from the top 25 documents retrieved from 74 questions and find that lists and faqs occur in more documents judged relevant to task-oriented questions than those judged relevant to fact-oriented questions.
automated index management for distributed web search. distributed heterogeneous search systems are an emerging phenomenon in web search, in which independent topic-specific search engines provide search services, and metasearchers distribute user's queries to only the most suitable search engines. previous research has investigated methods for engine selection and merging of search results (i.e. performance improvements from the user's perspective). we focus instead on performance from the service provider's point of view (e.g, income from queries processed vs. resources used to answer them). we consider a scenario in which individual search engines compete for user queries by choosing which documents (topics) to index. the difficulty here stems from the fact that the utilities of local engine actions should depend on the uncertain actions of competitors. thus, naive strategies (e.g, blindly indexing lots of popular documents) are ineffective. we model the competition between search engines as a stochastic game, and propose a reinforcement learning approach to managing search index contents. we evaluate our approach using a large log of user queries to 47 real search engines.
effective nearest neighbor indexing with the euclidean metric. the nearest neighbor search is an important operation widely-used in multimedia databases. in higher dimensions, most of previous methods for nearest neighbor search become inefficient and require to compute nearest neighbor distances to a large fraction of points in the space. in this paper, we present a new approach for processing nearest neighbor search with the euclidean metric, which searches over only a small subset of the original space. this approach effectively approximates clusters by encapsulating them into geometrically regular shapes and also computes better upper and lower bounds of the distances from the query point to the clusters. for showing the effectiveness of the proposed approach, we perform extensive experiments. the results reveal that the proposed approach significantly outperforms the x-tree as well as the sequential scan.
o-preh: optimistic transaction processing algorithm based on pre-reordering in hybrid broadcast environments. in recent years, there has been a lot of research effort in the periodic push model where the server repetitively disseminates information without explicit request. we call the broadcast model supporting backchannel as hybrid broadcast. in this paper, we devise a new transaction processing algorithm called o-preh, which is based on the notion of pre-reordering. if one or more conflicts for mobile transactions are found from server's periodic invalidation report, conflict orders are determined not to violate the consistency( pre-reordering) and then the remaining operations have to be executed pessimistically.
spatial match representation scheme supporting ranking in iconic images databases. because content-based image retrieval is essential to retrieve relevant multimedia documents, we represent images as a set of recognizable symbols, i.e., icon objects, and do indexing by regarding the icon object as a representative of a given document. when users request content-based image retrieval, we convert a query image into icon objects and retrieve relevant images in the database. in this paper, we propose a new spatial-match representation scheme, called srr(spatial-match representation supporting ranking) scheme, which combine directional operators with positional operators. therefore, our srr scheme can represent spatial relationships between icon objects precisely and can provide ranking for the retrieved images. in addition, we compare our scheme with the conventional 9dlt and smr schemes in terms of retrieval effectiveness. finally, we show from our experiment that our srr scheme holds about 25% higher recall and about 10% higher precision, compared with the 9dlt and the smr.
partial rollback in object-oriented/object-relational database management systems. in a database management system (dbms), partial rollback is an important mechanism for canceling only part of the operations executed in a transaction back to a savepoint. partial rollback complicates buffer management because it should restore the state of the buffers as well as that of the database. several relational dbmss (rdbmss) currently provide this mechanism using page buffers. however, object-oriented or object-relational dbmss (oo/ordbmss) cannot utilize the partial rollback scheme of rdbmss as is because, unlike rdbmss, many of them use a dual buffer consisting of an object buffer and a page buffer. in this paper, we propose a thorough study of partial rollback schemes of oo/ordbmss with a dual buffer. first, we classify the partial rollback schemes of oo/ordbmss into a single buffer-based scheme and a dual buffer-based scheme by the number of buffers used to process rollback. next, we propose four alternative partial rollback schemes: a page buffer-based scheme, an object buffer-based scheme, a dual buffer-based scheme using a soft log, and a dual buffer-based scheme using shadows. we then evaluate their performance through simulations. the results show that the dual buffer-based partial rollback scheme using shadows provides the best performance. partial rollback in oo/ordbms has not been addressed in the literature; yet, it is a useful mechanism that must be implemented. the proposed schemes are practical ones that can be implemented in such dbmss.
functionality and architecture of a cooperative database system: a vision. a database system fostering the cooperative usage and modification of a common data pool should provide standard database functionality (e.g. application-independent correctness criteria and data modelling) plus means for a step-wise, cooperative refinement of data over a long period of time. key ingredients are a hierarchical organization of work, a sound data model covering cooperative uncertainly, and support for long-living cooperative processes. furthermore, mechanisms for data passing and hiding, negotiation means, and notification are of prominent importance. in the paper, the rationale behind such a functionality is described. furthermore, a proposal is made for the software architecture of what is called a cooperative database system (cdbms).
finding interesting rules from large sets of discovered association rules. association rules, introduced by agrawal, imielinski, and swami, are rules of the form &ldquo;for 90% of the rows of the relation, if the row has value 1 in the columns in set w, then it has 1 also in column b&rdquo;. efficient methods exist for discovering association rules from large collections of data. the number of discovered rules can, however, be so large that browsing the rule set and finding interesting rules from it can be quite difficult for the user. we show how a simple formalism of rule templates makes it possible to easily describe the structure of interesting rules. we also give examples of visualization of rules, and show how a visualization tool interfaces with rule templates.
processing search queries in a distributed environment. endeca's approach to processing search queries in a distributed computing environment is predicated on concerns of correctness, scalability, and flexibility in deployment. using a master-slave architecture, we are able to support classic search as well as more advanced features. we avoid bottlenecking the master with excessive computation or communication by limiting the information from the slaves in the expected case.
summarization as feature selection for text categorization. we address the problem of evaluating the effectiveness of summarization techniques for the task of document categorization. it is argued that for a large class of automatic categorization algorithms, extraction-based document categorization can be viewed as a particular form of feature selection performed on the full text of the document and, in this context, its impact can be compared with state-of-the-art feature selection techniques especially devised to provide good categorization performance. such a framework provides for a better assessment of the expected performance of a categorizer if the compression rate of the summarizer is known.
query workload-aware overlay construction using histograms. peer-to-peer(p2p) systems over an efficient means of data sharing among a dynamically changing set of a large number of a tonomous nodes.each node in a p2p system is connected with a small number of other nodes thus creating an overlay network of nodes. a query posed at a node is routed through the overlay network towards nodes hosting data items that satisfy it. in this paper, we consider building overlays that exploit the query workload so that nodes are clustered based on their results to a given query workload. the motivation is to create overlays where nodes that match a large number of similar queries are a fewlinks apart. query frequency is also taken into account so that popular queries have a greater effect on the formation of the overlay than unpopular ones. we focus on range selection queries and se histograms to estimate the query results of each node. then, nodes are clustered based on the similarity of their histograms. to this end,we introd ce a workload-aware edit distance metric between histograms that takes into account the query workload. our experimental results show that workload-aware overlays increase the percentage of query results returned for a given number of nodes visited as compared to both random (i.e., unclustered)overlays and non workload-aware clustered overlays (i.e., overlays that cluster nodes based solely on the nodes' content).
leveraging collective knowledge. as more organizations begin to deploy taxonomies for categorization and faceted search, the cost of producing these knowledge models is becoming the largest expense on a project. at a cost of 200 - 300 dollars per topic, manually developing subject area taxonomies does not scale for any but the smallest of projects. this paper will discuss an approach called orthogonal corpus indexing ( oci ). oci leverages existing published knowledge in the subject area of the taxonomy model. this knowledge is algorithmically mapped into multiple taxonomies via the oci algorithm. the resulting taxonomy costs are 1/ 100th of the cost of manual methods and are created with embedded rule sets for categorization engines. this paper will discuss the theory of oci, its practical use as well as examples of knowledge management techniques that are possible when taxonomies are large, detailed and inexpensive.
stemming and lemmatization in the clustering of finnish text documents. stemming and lemmatization were compared in the clustering of finnish text documents. since finnish is a highly inflectional and agglutinative language, we hypothesized that lemmatization, involving splitting of the compound words, would be more appropriate normalization approach than the straightforward stemming. the relevance of the documents were evaluated with a four-point relevance assessment scale, which was collapsed into binary one by considering all the relevant and only the highly relevant documents relevant, respectively. experiments with four hierarchical clustering methods supported the hypothesis. the stringent relevance scale showed that lemmatization allowed the single and complete linkage methods to recover especially the highly relevant documents better than stemming. in comparison with stemming, lemmatization together with the average linkage and ward's methods produced higher precision. we conclude that lemmatization is a better word normalization method than stemming, when finnish text documents are clustered for information retrieval.