waashk/dblp huggingface dataset:
towards a framework for integrating multilevel secure models and temporal data models. within many organizations the number of databases containing classified or otherwise sensitive data is increasing rapidly. access to these databases must be restricted and controlled to limit the unauthorized disclosure, or malicious modification of data contained in them. however, the conventional models of authorization that have been designed for database systems supporting the hierarchical, network and relational models of data do not provide adequate mechanisms to support controlled access of temporal objects and context based temporal information. in this paper we extend the multilevel secure relational model to capture the functionality required of a temporal database, i.e. a database that supports some aspect of time, not counting user-defined time. in particular we assign class access to bitemporal timestamped attributes, and give explicit security classifications to temporal elements.
building information systems for mobile environments. it is expected that in the near future, tens of millions of users will have access to distributed information systems through wireless connections. the technical characteristics of the wireless medium and the resulting mobility of both data resources and data consumers raise new challenging questions regarding the development of information systems appropriate for mobile environments. in this paper, we report on the development of such a system. first, we describe the general architecture of the information system and the main considerations of our design. then, based on these considerations, we present our system support for maintaining the consistency of replicated data and for providing transaction schemas that account for the frequent but predictable disconnections, the mobility, and the vulnerability of the wireless environment.
mining soft-matching association rules. variation and noise in database entries can prevent data mining algorithms, such as association rule mining, from discovering important regularities. in particular, textual fields can exhibit variation due to typographical errors, mispellings, abbreviations, etc.. by allowing partial or "soft matching" of items based on a similarity metric such as edit-distance or cosine similarity, additional important patterns can be detected. this paper introduces an algorithm, softapriori that discovers soft-matching association rules given a user-supplied similarity metric for each field. experimental results on several "noisy" datasets extracted from text demonstrate that softapriori discovers additional relationships that more accurately reflect regularities in the data.
providing consistent and exhaustive relevance assessments for xml retrieval evaluation. comparing retrieval approaches requires test collections, which consist of documents, queries and relevance assessments. obtaining consistent and exhaustive relevance assessments is crucial for the appropriate comparison of retrieval approaches. whereas the evaluation methodology for flat text retrieval approaches is well established, the evaluation of xml retrieval approaches is a research issue. this is because xml documents are composed of nested components that cannot be considered independent in terms of relevance. this paper describes the methodology adopted in inex (the initiative for the evaluation of xml retrieval) to ensure consistent and exhaustive relevance assessments.
capturing term dependencies using a language model based on sentence trees. we describe a new probabilistic sentence tree language modeling approach that captures term dependency patterns in topic detection and tracking's (tdt) story link detection task. new features of the approach include modeling the syntactic structure of sentences in documents by a sentence-bin approach and a computationally efficient algorithm for capturing the most significant sentence-level term dependencies using a maximum spanning tree approach, similar to van rijsbergen's modeling of document-level term dependencies.the new model is a good discriminator of on-topic and off-topic story pairs providing evidence that sentence-level term dependencies contain significant information about relevance. although runs on a subset of the tdt2 corpus show that the model is outperformed by the unigram language model, a mixture of the unigram and the sentence tree models is shown to improve on the best performance especially in the regions of low false alarms.
relevant query feedback in statistical language modeling. in traditional relevance feedback, researchers have explored relevant document feedback, wherein, the query representation is updated based on a set of relevant documents returned by the user. in this work, we investigate relevant query feedback, in which we update a document's representation based on a set of relevant queries. we propose four statistical models to incorporate relevant query feedback.to validate our models, we considered anchor text of incoming links to a given document as feedback queries and performed experiments on the home-page retrieval task of trec 2001. our results show that three of our four models outperform the query-likelihood baseline by at least 35% in mrr score on a test set.
data fusion with estimated weights. this paper proposes an adptive approach for data fusion of information retrieval systems, which exploits estimated performances of all component input systems without relevance judgement or training. the estimation is conducted prior to the fusion but uses the same data as fusion applies. the experiment shows that our algorithms are competitive with, and often outperform combmnz, one of the most effective algorithms in use.
event threading within news topics. with the overwhelming volume of online news available today, there is an increasing need for automatic techniques to analyze and present news to the user in a meaningful and efficient manner. previous research focused only on organizing news stories by their topics into a flat hierarchy. we believe viewing a news topic as a flat collection of stories is too restrictive and inefficient for a user to understand the topic quickly. in this work, we attempt to capture the rich structure of events and their dependencies in a news topic through our event models. we call the process of recognizing events and their dependencies <i>event threading</i>. we believe our perspective of modeling the structure of a topic is more effective in capturing its semantics than a flat list of on-topic stories. we formally define the novel problem, suggest evaluation metrics and present a few techniques for solving the problem. besides the standard word based features, our approaches take into account novel features such as temporal locality of stories for event recognition and time-ordering for capturing dependencies. our experiments on a manually labeled data sets show that our models effectively identify the events and capture dependencies among them.
mining generalised disjunctive association rules. this paper introduces generalised disjunctive association rules such as "people who buy bread also buy butter jam", and "people who buy either raincoats or umbrellas also buy flashlights". a generalised disjunctive association rule allows the disjunction of conjuncts, "people who buy jackets also buy bow ties or neckties and tiepins". such rules capture contextual inter-relationships among items.given a context (antecedent), there may be a large number of generalised disjunctive association rules that satisfy the minsupp and minconf constraints. it is computationally expensive to find all such rules. we present algorithm thrifty traverse which borrows concepts such as subsumption from propositional logic to mine a subset of such rules in a computationally feasible way. we experimented with our algorithm on us census data as well as transaction data from a grocery superstore to demonstrate its computational feasibility, utility and scalability.
document clustering based on cluster validation. this paper presents a cluster validation based document clustering algorithm, which is capable of identifying both important feature words and true model order (cluster number). important feature subset is selected by optimizing a cluster validity criterion subject to some constraint. for achieving model order identification capability, this feature selection procedure is conducted for each possible value of cluster number. the feature subset and cluster number which maximize the cluster validity criterion are chosen as our answer. we have applied our algorithm to several datasets from 20newsgroup corpus. experimental results show that our algorithm can find important feature subset, estimate the model order and yield higher micro-averaged precision than other four document clustering algorithms which require cluster number to be provided.
distance-function design and fusion for sequence data. sequence-data mining plays a key role in many scientific studies and real-world applications such as bioinformatics, data stream, and sensor networks, where sequence data are processed and their semantics interpreted. in this paper we address two relevant issues: sequence-data representation, and representation-to-semantics mapping. for representation, since the best one is dependent upon the application being used and even the type of query, we propose representing sequence data in multiple views. for each representation, we propose methods to construct a <i>valid kernel</i> as the distance function to measure <i>similarity</i> between sequences. for mapping, we then find the best combination of the individual distance functions, which measure similarity of different views, to depict the target semantics. we propose a <i>super-kernel function-fusion</i> scheme to achieve the optimal mapping. through theoretical analysis and empirical studies on uci and real world datasets, we show our approach of multi-view representation and fusion to be mathematically valid and very effective for practical purposes.
an efficient and effective algorithm for density biased sampling. in this paper we describe a new density-biased sampling algorithm. it exploits spatial indexes and the local density information they preserve, to provide improved quality of sampling result and fast access to elements of the dataset. it attains improved sampling quality, with respect to factors like skew, noise or dimensionality. moreover, it has the advantage of efficiently handling dynamic updates, and it requires low execution times. the performance of the proposed method is examined experimentally. the comparative results illustrate its superiority over existing methods.
selectivity-based partitioning: a divide-and-union paradigm for effective query optimization. modern query optimizers select an efficient join ordering for a physical execution plan based essentially on the average join selectivity factors among the referenced tables. in this paper, we argue that this "monolithic" approach can miss important opportunities for the effective optimization of relational queries. we propose selectivity-based partitioning, a novel optimization paradigm that takes into account the join correlations among relation fragments in order to essentially enable multiple (and more effective) join orders for the evaluation of a single query. in a nutshell, the basic idea is to carefully partition a relation according to the selectivities of the join operations, and subsequently rewrite the query as a union of constituent queries over the computed partitions. we provide a formal definition of the related optimization problem and derive properties that characterize the set of optimal solutions. based on our analysis, we develop a heuristic algorithm for computing efficiently an effective partitioning of the input query. results from a preliminary experimental study verify the effectiveness of the proposed approach and demonstrate its potential as an effective optimization technique.
automatic discovery of salient segments in imperfect speech transcripts. this paper addresses the problem of automatic detection of salient video segments for real-world applications such as corporate training based on associated speech transcriptions. we present a novel segmentation algorithm based on automatic speech recognition (asr) applied to the audio track of the video. our feature set consists of word n-grams extracted from the imperfect speech transcriptions. we use a two-pass algorithm that combines a boundary-based method with a content-based method. in the first pass, we analyze the temporal distribution and the rate of arrival of features to compute an initial segmentation. in the second pass, we detect changes in content-bearing words by using the content-bearing features as queries in an information retrieval system. the content-based second pass validates the initial segments and merges them as needed. variations in the structure of the audio/video content, and the accuracy of asr have an impact on the feasibility of the segmentation task. for realistic data we observe that we can identify content-rich segments of the audio. in the best scenario a high-level table-of-contents is generated and in the worse scenario a single salient segment is identified. we illustrate the algorithm in detail with some examples and validate the data with manual segmentation boundaries.
dsac: integrity for outsourced databases with signature aggregation and chaining. database outsourcing is an important trend which involves data owners farming out their data management needs to an external service provider. one important requirement is to maintain the integrity and authenticity of outsourced data. whenever an outsourced database is queried, the corresponding query reply must be demonstrably authentic. furthermore, a reply must include a proof of completeness to convince the querier that no data matching the query predicate(s) has been omitted. in this paper, we suggest new techniques in support of efficient authenticity and completeness guarantees of such query replies.
maximal termsets as a query structuring mechanism. search engines process queries conjunctively to restrict the size of the answer set. further, it is not rare to observe a mismatch between the vocabulary used in the text of web pages and the terms used to compose the web queries. the combination of these two features might lead to irrelevant query results, particularly in the case of more specific queries composed of three or more terms. to deal with this problem we propose a new technique for automatically structuring web queries as a set of smaller subqueries. to select representative subqueries we use information on their distributions in the document collection. this can be adequately modeled using the concept of maximal termsets derived from the formalism of association rules theory. experimentation shows that our technique leads to improved results. for the trec-8 test collection, for instance, our technique led to gains in average precision of roughly 28% with regard to a bm25 ranking formula.
xml, the web and database functionality?. with the advance of xml, dtd's, xml-schema, x-path, x-link, xml-query, xml-protocol, rdf, semantic web and other information description tools and mechanisms the web develops more and more into a huge structured information and data network that becomes computer processable via "semi"-automatic "intelligent" means.however, most of these multimedia information resources are kept in hierarchical file-type systems or relatively unstructured relational systems with none or very little of what is commonly known as "database functionality" for the various information types represented. do we still need transaction properties, interoperability, security, reliability, scalability and others?if yes, then why are these concepts not really taken into the web-world?are the database people too far away to be able to adopt their existing solutions?are the information system people too process-oriented to care about data?are the knowledge handlers too occupied with semantic representations and ontology discussions?does the document community even see the problem?and do the web people care whether human resources are wasted through attempts to achieve database functionality through careful "behaviour"?the panel will offer opinions about these aspects but will also want to raise these issues with the audience.
towards integrative enterprise knowledge portals. knowledge portals make an important contribution to enabling enterprise knowledge management by providing users with a consolidated, personalized user interface that allows efficient access to various types of (structured and unstructured) information. today's portal systems allow combining access modules to different information sources side by side on a single portal webpage. however, there is no interaction between those so called portlets. when a user navigates within one portlet, the others remain unchanged, which means that each source has to be searched individually for relevant information.this paper discusses integration aspects within enterprise knowledge portals and presents an approach for communicating the user context (revealing the user's information need) among portlets, utilizing semantic web technologies. for example, the query context of an olap portlet, which provides access to structured data stored in a data warehouse, can be used by an information retrieval portlet in order to automatically provide the user with related documents found in the organization's document management system. the paper shortly presents a prototype that we are building to evaluate our approach, demonstrating such an olap and information retrieval integration.
ready for prime time: pre-generation of web pages in tiscover. in large data- and access-intensive web sites, efficient and reliable access is hard to achieve. this situation gets even worse for web sites providing precise structured query facilities and requiring topicality of the presented information even in face of a highly dynamic content. the achievement of these partly conflicting goals is strongly influenced by the approach chosen for page generation, ranging from composing a web page upon a user's request to its generation in advance. the official austrian web-based tourism information and booking system tiscover tries to reconcile these goals by employing a hybrid approach of page generation. in tiscover, web pages are not only generated on request in order to support precise structured queries on the content managed by a database system. rather, the whole web site is also pre-generated out of the extremely dynamic content and synchronized with the database on the basis of metadata. thus, topicality of information is guaranteed, while ensuring efficient and reliable access. this paper discusses the hybrid approach as realized in tiscover, focussing in particular on the concepts used for pre-generation.1
multiversion divergence control of time fuzziness. epsilon serializability (esr) has been proposed to manage and control inconsistency in extending the classic transaction processing. esr increases system concurrency by tolerating a bounded amount of inconsistency. in this paper, we present multiversion divergence control (mvdc) algorithms that support esr with not only value but also time fuzziness in multiversion databases. unlike value fuzziness, accumulating time fuzziness is semantically different. a simple summation of the length of two time intervals may either underestimate the total time fuzziness, resulting in incorrect execution, or overestimate the total time fuzziness, unnecessarily degrading the effectiveness of mvesr. we present a new operation, called timeunion, to accurately accumulate the total time fuzziness. because of the accurate control of time and value fuzziness by the mvdc algorithm, mvesr is very suitable for the use of multiversion databases for real-time applications that may tolerate a limited degree of data inconsistency but prefer more data recency.
a function-based access control model for xml databases. xml documents are frequently used in applications such as business transactions and medical records involving sensitive information. typically, parts of documents should be visible to users depending on their roles. for instance, an insurance agent may see the billing information part of a medical document but not the details of the patient's medical history. access control on the basis of data location or value in an xml document is therefore essential. in practice, the number of access control rules is on the order of millions, which is a product of the number of document types (in 1000's) and the number of user roles (in 100's). therefore, the solution requires high scalability and performance. current approaches to access control over xml documents have suffered from scalability problems because they tend to work on individual documents. in this paper, we propose a novel approach to xml access control through rule functions that are managed separately from the documents. a rule function is an executable code fragment that encapsulates the access rules (paths and predicates), and is shared by all documents of the same document type. at runtime, the rule functions corresponding to the access request are executed to determine the accessibility of document fragments. using synthetic and real data, we show the scalability of the scheme by comparing the accessibility evaluation cost of two rule function models. we show that the rule functions generated on user basis is more efficient for xml databases.
rhist: adaptive summarization over continuous data streams. maintaining approximate aggregates and summaries over data streams is crucial to handle the olap query workload that arises in applications, such as network monitoring and telecommunications. furthermore, since the entire data is not available at all times the maintenance task must be done incrementally. we show that r(elaxed)hist(ogram) is an appropriate summarization under data stream scenario. in order to reduce query estimation errors, we propose adaptive approaches which not only capture the data distribution, but also integrate independent query patterns. we introduce a workload decay model to efficiently capture global workload information and ensure that the query patterns from the recent past are weighted more than queries that are further in the past. we verify experimentally that our approach successfully adapts to continuously changing workload as well as data streams.
index filtering and view materialization in rolap environment. using materialized view to accelerate olap queries is one of the most common methods used in rolap systems. however, high storage and computation cost make this method very difficult to be implemented in the actual environment. among various issues associated with this, index selection and view materialization are two of the top challenges. in this paper, we propose to build indexes on subsets of the primary keys rather than the full sets if the index selectivity for these smaller indexes can be maintained above the required level. based on that we propose an index filtering rule, dominant prime (dprime) index set filter, to filter out candidate indexes that have insufficient index selectivity or have cheaper alternatives. in the second part, we propose a view materialization method, nested relation approach, to group tuples with the same value for index attributes into one super tuple using a nested relation and implement this method using oracle varray. in performance tests, our method outperforms others significantly.