waashk/dblp huggingface dataset:
situation-aware risk management in autonomous agents. we present a novel approach to enable decision-making in a highly distributed multiagent environment where individual agents need to act in an autonomous fashion. our architecture framework integrates risk management, knowledge management, and agent deliberation to enable sophisticated, autonomous decision-making. instead of a centralized knowledge repository, our approach supports a highly distributed knowledge base in which each agent manages a fraction of the knowledge needed by the entire system.
localized routing trees for query processing in sensor networks. in this paper, we propose a novel energy-efficient approach, a localized routing tree (lrt) coupled with a route redirection (rr) strategy, to support various types of queries. lrts take care of the sensors near the sink and reduce the energy consumption of these sensors, and rr reduces the energy cost of data receptions. compared to the existing approaches, simulation studies show that lrt together with rr has significant improvement on the query capacity.
automatically extracting structure and data from business reports. a considerable amount of clean semistructured data is internally available to companies in the form of business reports. however, business reports are untapped for data mining, data warehousing, and querying because they are not in relational form. business reports have a regular structure that can be reconstructed. we present algorithms that automatically infer the regular structure underlying business reports and automatically generate wrappers to extract relational data.
the link prediction problem for social networks. given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? we formalize this question as the link prediction problem, and develop approaches to link prediction based on measures the "proximity" of nodes in a network. experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.
typed functional query languages with equational specifications. we present a framework for functionally modeling query languages and data models. data and queries are uniformly represented by first-order functions, and query-language constructs by polymorphic higher-order functions. the functions are typed by a database-oriented type system that supports polymorphism and nesting of types, thus one can perform static type-checking and type-inferencing of query-expressions. the query language can be freely extended by introducing new querying constructs as polymorphic higher-order functions.while type information gives the input-output description of the functions, the semantic information is captured by equational specifications. knowledge about the functions is represented as equalities of functional expressions in the form of equations. by equational axiomatization of the query language, database problems of query equivalence and answering-query with views can be posed as equational word-problems and equational matching.
a horizontal fragmentation algorithm for the fact relation in a distributed data warehouse. data warehousing is one of the major research topics of appliedside database investigators. most of the work to date has focused on building large centralized systems that are integrated repositories founded on pre-existing systems upon which all corporate-wide data are based. unfortunately, this approach is very expensive and tends to ignore the advantages realized during the past decade in the area of distribution and support for data localization in a geographically dispersed corporate structure. this research investigates building distributed data warehouses with particular emphasis placed on distribution design for the data warehouse environment. the article provides an architectural model for a distributed data warehouse, the formal definition of the relational data model for data warehouse and a methodology for distributed data warehouse design along with a &ldquo;horizontal&rdquo; fragmentation algorithm for the fact relation.
qfilter: fine-grained run-time xml access control via nfa-based query rewriting. at present, most of the state-of-the-art solutions for xml access controls are either (1) document-level access control techniques that are too limited to support fine-grained security enforcement; (2) view-based approaches that are often expensive to create and maintain; or (3) impractical proposals that require substantial security-related support from underlying xml databases. in this paper, we take a different approach that assumes no security support from underlying xml databases and examine three alternative fine-grained xml access control solutions, namely <i>primitive, pre-processing</i> and <i>post-processing</i> approaches. in particular, we advocate a pre-processing method called <i>qfilter</i> that uses non-deterministic finite automata (nfa) to rewrite user's query such that any parts violating access control rules are pruned. we show the construction and execution of a qfilter and demonstrate its superiority to other competing methods.
image similarity search with compact data structures. the recent theoretical advances on compact data structures (also called "sketches") have raised the question of whether they can effectively be applied to content-based image retrieval systems. the main challenge is to derive an algorithm that achieves high-quality similarity searches while using compact metadata. this paper proposes a new similarity search method consisting of three parts. the first is a new region feature representation with weighted $=<i></i><inf>1</inf> distance function, and emd* match, an improved emd match, to compute image similarity. the second is a thresholding and transformation algorithm to convert feature vectors into very compact data structures. the third is an emd embedding based filtering method to speed up the query process. we have implemented a prototype system with the proposed method and performed experiments with a 10,000 image database. our results show that the proposed method can achieve more effective similarity searches than previous approaches with metadata 3 to 72 times more compact than previous systems. the experiments also show that our emd embedding based filtering technique can speed up the query process by a factor of 5 or more with little loss in query effectiveness.
learning probabilistic datalog rules for information classification and transformation. probabilistic datalog is a combination of classical datalog (i.e., function-free horn clause predicate logic) with probability theory. therefore, probabilistic weights may be attached to both facts and rules. but it is often impossible to assign exact rule weights or even to construct the rules themselves. instead of specifying them manually, learning algorithms can be used to learn both rules and weights. in practice, these algorithms are very slow because they need a large example set and have to test a high number of rules. we apply a number of extensions to these algorithms in order to improve efficiency. several applications demonstrate the power of learning probabilistic datalog rules, showing that learning rules is suitable for low dimensional problems (e.g., schema mapping) but inappropriate for higher dimensions like e.g. in text classification.
a multi-system analysis of document and term selection for blind feedback. experiments were conducted to explore the impact of combining various components of eight leading information retrieval systems. each system demonstrated improved effectiveness with the use of <i>blind feedback</i>, in which the results of a preliminary retrieval step were used to augment the efficacy of a secondary retrieval step. the hybrid combination of primary and secondary retrieval steps from different systems in a number of cases yielded better effectiveness than either of the constituent systems alone. this positive combining effect was observed when entire documents were passed between the two retrieval steps, but not when only the expansion terms were passed. several combinations of primary and secondary retrieval steps were fused using the combmnz algorithm; all yielded significant effectiveness improvement over the individual systems, with the best yielding a an improvement of 13% (<i>p</i> = 10<sup>-6</sup>) over the best individual system and an improvement of 4% (<i>p</i> = 10<sup>-5</sup>) over a simple fusion of the eight systems.
information retrieval and machine learning for probabilistic schema matching. schema matching is the problem of finding correspondences (mapping rules, e.g. logical formulae) between heterogeneous schemas e.g. in the data exchange domain, or for distributed ir in federated digital libraries. this paper introduces a probabilistic framework, called splmap, for automatically learning schema mapping rules, based on given instances of both schemas. different techniques, mostly from the ir and machine learning fields, are combined for finding suitable mapping candidates. our approach gives a probabilistic interpretation of the prediction weights of the candidates, selects the rule set with highest matching probability, and outputs probabilistic rules which are capable to deal with the intrinsic uncertainty of the mapping process. our approach with different variants has been evaluated on several test sets.
extracting unstructured data from template generated web documents. we propose a novel approach that identifies web page templates and extracts the unstructured data. extracting only the body of the page and eliminating the template increases the retrieval precision for the queries that generate irrelevant results. we believe that by reducing the number of irrelevant results; the users are encouraged to go back to a given site to search. our experimental results on several different web sites and on the whole cnnfn collection demonstrate the feasibility of our approach.
rstar: an rdf storage and query system for enterprise resource management. modern corporations operate in an extremely complex environment and strongly depend on all kinds of information resources across the enterprise. unfortunately, with the growth of an enterprise, its information resources are not only heterogeneous but also distributed in physically different systems and databases. how to effectively exploit information across the enterprise is becoming a critical but hard problem. in recent years, metadata which is the detailed description of the data is used to efficiently exploit information resources in the web. the world wide web consortium (w3c) recommends the resource description framework (rdf) as a standard for the definition and use of metadata descriptions of resources in the web. in this paper, we present an rdf storage and query system called rstar for enterprise resource management. rstar uses a relational database as the persistent data store and defines rstar query language (rsql) for resource retrieval. currently, most of existing rdf storage and query systems are evaluated on small data sets and no detailed performance analysis is given for such systems. therefore, we conduct extensive experiments on a large scale data set to investigate the performance problem in rdf storage. such analysis will be helpful for designing rdf storage and query systems as well as for understanding not well-solved issues in rdf based enterprise resource management. in addition, experiences and lessons learned in our implementation are presented for further research and development.
a framework for refining similarity queries using learning techniques. in numerous applications that deal with similarity search, a user may not have an exact idea of his information need and/or may not be able to construct a query that exactly captures his notion of similarity. a promising approach to mitigate this problem is to enable the user to submit a rough approximation of the desired query and use the feedback on the relevance of the retrieved objects to refine the query. in this paper, we explore such a refinement strategy for a general class of sql similarity queries. our approach casts the refinement problem as that of learning concepts using examples. this is achieved by viewing the tuples on which a user provides feedback as a labeled training set for a learner. under this setup, sql query refinement consists of two learning tasks, namely learning the structure of the sql query and learning the relative importance of the query components. the paper develops appropriate machine learning approaches suitable for these two learning tasks. the primary contribution of the paper is a general refinement framework that decides when each learner is invoked in order to quickly learn the user query. experimental analyses over many real life datasets and queries show that our strategy outperforms the existing approaches significantly in terms of retrieval accuracy and query simplicity.
ontologies for semantically interoperable systems. in this paper, we discuss the use of ontologies for semantic interoperability and integration. we argue that information technology has evolved into a world of largely loosely coupled systems and as such, needs increasingly more explicit, machine-interpretable semantics. ontologies in the form of logical domain theories and their knowledge bases offer the richest representations of machine-interpretable semantics for systems and databases in the loosely coupled world, thus ensuring greater semantic interoperability and integration. finally, we discuss how ontologies support semantic interoperability in the real, commercial and governmental world.
adding numbers to text classification. many real-world problems involve a combination of both text- and numerical-valued features. for example, in email classification, it is possible to use instance representations that consider not only the text of each message, but also numerical-valued features such as the length of the message or the time of day at which it was sent. text-classification methods have thus far not easily incorporated numerical features. in earlier work we described an approach for converting numerical features into bags of tokens so that text classification methods can be applied to numerical classification problems, and showed that the resulting learning methods are competitive with traditional numerical classification methods. in this paper we use this as a way to learn on problems that involve a combination of text and numbers. we show that the results outperform competing methods. further, we show that selecting a best classification method using text-only features and then adding numerical features to the problem (as might happen if numerical features are only later added to a pre existing text-classification problem) gives performance that rivals a more time-consuming approach of evaluating all classification methods using the full set of both text and numerical features.
xml parsing: a threat to database performance. xml parsing is generally known to have poor performance characteristics relative to transactional database processing. yet, its potentially fatal impact on overall database performance is being underestimated. we report real-word database applications where xml parsing performance is a key obstacle to a successful xml deployment. there is a considerable share of xml database applications which are prone to fail at an early and simple road block: xml parsing. we analyze xml parsing performance and quantify the extra overhead of dtd and schema validation. comparison with relational database performance shows that the desired response times and transaction rates over xml data can not be achieved without major improvements in xml parsing technology. thus, we identify research topics which are most promising for xml parser performance in database systems.
dynamic extraction topic descriptors and discriminators: towards automatic context-based topic search. effective knowledge management may require going beyond initial knowledge capture, to support decisions about how to extend previously-captured knowledge. electronic <i>concept maps,</i> interlinked with other concept maps and multimedia resources, can provide rich <i>knowledge models</i> for human knowledge capture and sharing. this paper presents research on methods for supporting experts as they extend these knowledge models, by searching the web for new context-relevant topics as candidates for inclusion. this topic search problem presents two challenges: first, how to formulate queries to seek topics that reflect the context of the current knowledge model, and, second, how to identify candidate topics with the right balance of novelty and relevance. more generally, this problem raises the broad question of the interaction of topic information from the local analysis space (a collected set of documents) and the global search space (the web). the paper develops a framework for understanding this interaction, and proposes and evaluates techniques for addressing the query formation and topic identification questions by dynamically extracting topic descriptors and discriminators from a knowledge model, to characterize information needs for retrieval and filtering of relevant material. using these techniques, we have developed a support tool that starts from a knowledge model under construction and automatically produces a set of suggestions for topics to include, proactively supporting users as they extend knowledge models.
the effectiveness of query expansion for distributed information retrieval. query expansion has been shown effective for both single database retrieval and for distributed information retrieval where complete collection information is available. one might expect that query expansion would then work for distributed information retrieval when complete collection information is not available. however, this does not appear to be the case. when using local context analysis for query expansion in distributed retrieval with partial information, the most significant reason query expansion does not work is that merging scores of documents retrieved by expanded queries is very difficult. however, we have found that using sampled information for query expansion can give boosts in a single database environment, and that when more information is available, query expansion can work in distributed environments. we also show that most of the benefit of query expansion in distributed retrieval comes from finding good documents, and not from selecting good databases.
sampling from databases using b+-trees. sampling techniques are becoming increasingly important for very large databases. however, the problem of obtaining a random sample from index structures has not received much attention. in this paper, we examine sampling techniques for b^+-tree. as the fanout of each node varies, a random walk through the index structure does not produce a good representative sample of the data set. we propose a new technique, called b^+-tree based weighted random sampling (btwrs), that alters the inclusion probabilities of records accordingly to allow more records from leaves, along the paths with higher fanouts, to be extracted. we extensively evaluated our method, and the results show that there is an improvement in btwrs over the existing schemes in terms of the quality of the samples obtained and the efficiency of the sampling process. the proposed method can be readily adopted in existing commercial systems.
foci: flexible organizer for competitive intelligence. this paper describes how an integrated web-based application, code-named foci (flexible organizer for competitive intelligence), can help the knowledge worker in the gathering, organizing, tracking, and dissemination of competitive intelligence or knowledge bases on the web. it shows how text mining techniques including a novel user-configurable clustering, trend analysis and visualization techniques can be used synergistically to address the problem of managing information gathered from the web. foci allows a user to define and personalize the organization of the information clusters according to their needs and preferences into portfolios. predefined sections for organizing information in specific domains is also supported. the personalized portfolios created can be saved and subsequently tracked and shared with other users. in addition, foci is designed to handle multilingual documents.
versatile structural disambiguation for semantic-aware applications. in this paper, we propose a versatile disambiguation approach which can be used to make explicit the meaning of structure based information such as xml schemas, xml document structures, web directories, and ontologies. it can be of support to the semantic-awareness of a wide range of applications, from schema matching and query rewriting to peer data management systems, from xml data clustering to ontology-based automatic annotation of web pages and query expansion. the effectiveness of the achieved results has been experimentally proved and is founded both on a flexible exploitation of the structure context, whose extraction can be tailored on the specific application needs, and of the information provided by commonly available thesauri such as wordnet.
light-weight xpath processing of xml stream with deterministic automata. several applications based on xml stream processing have recently emerged, such as those for air traffic control and the selective dissemination of information (sdi). their common need is to process a large number of xpath expressions in continuous xml streams at high throughput.this paper proposes four techniques for xpath expression processing based on deterministic finite automata (dfa) for two purposes: to improve the memory usage efficiency of the automata and to support the processing of branching xpath expressions. the first technique, called n-dfa, clusters the given xpath expressions into n clusters to reduce the number of dfa states. the second, called shared nfa state table, lets the non-deterministic finite automata (nfa) state set be shared among the dfa states. our experiments show that memory usage in an 8-dfa can, with the shared nfa state table, be reduced to 1/40th that of the original 1-dfa. the optimized nfa conversion and general xpath expression processing algorithm techniques contribute to the processing of branching xpath expressions efficiently; overall performance is better than is possible with earlier approaches.
a syntactic approach for searching similarities within sentences. textual data is the main electronic form of knowledge representation. sentences, meant as logic units of meaningful word sequences, can be considered its backbone. in this paper, we propose a solution based on a purely syntactic approach for searching similarities within sentences, named approximate sub2sequence matching. this process being very time consuming, efficiency in retrieving the most similar parts available in large repositories of textual data is ensured by making use of new filtering techniques. as far as the design of the system is concerned, we chose a solution that allows us to deploy approximate sub2 sequence matching without changing the underlying database.
frem: fast and robust em clustering for large data sets. clustering is a fundamental data mining technique. this article presents an improved em algorithm to cluster large data sets having high dimensionality, noise and zero variance problems. the algorithm incorporates improvements to increase the quality of solutions and speed. in general the algorithm can find a good clustering solution in 3 scans over the data set. alternatively, it can be run until it converges. the algorithm has a few parameters that are easy to set and have defaults for most cases. the proposed algorithm is compared against the standard em algorithm and the on-line em algorithm.