Relevant Queries Research Articles

An algorithm for differential privacy streaming data publication based on matrix mechanism under exponential decay mode

At present, many practical applications require the continuous release of statistical streaming data, and the importance of current data is higher than historical data. The solution to this problem is to assign weights to the data and propose a differential privacy data release method under exponential decay. However, existing methods only consider a single query, and cannot effectively use the correlation between queries in the continuous statistical publishing background to further improve the accuracy of the query. In this paper, we present a differential privacy data release algorithm (DMFDA) in exponential decay mode based on a matrix mechanism, which uses the advantages of the matrix to deal with relevant queries. Firstly, we use the construction method to generate the matrix decomposition strategy to meet the real-time requirements of streaming data. Secondly, the diagonal matrix is used to adjust the structure of the constructed strategy matrix so as to improve the release accuracy. Finally, according to the substructure of the constructed strategy matrix, a fast method of solving the diagonal matrix is proposed. The experiment is designed to compare DMFDA and similar algorithms for streaming data release in exponential decay. Experimental results show that the DMFDA algorithm is effective and feasible.

Learning Online Trends for Interactive Query Auto-Completion

Query auto-completion (QAC) is widely used by modern search engines to assist users by predicting their intended queries. Most QAC approaches rely on deterministic batch learning algorithms trained from past query log data. However, query popularities keep changing all the time and QAC operates in a real-time scenario where users interact with the search engine continually. So, ideally, QAC must be timely and adaptive enough to reflect time-sensitive changes in an online fashion. Second, due to the vertical position bias, a query suggestion with a higher rank tends to attract more clicks regardless of user’s original intention. Hence, in the long run, it is important to place some lower ranked yet potentially more relevant queries to higher positions to collect more valuable user feedbacks. In order to tackle these issues, we propose to formulate QAC as a ranked Multi-Armed Bandits (MAB) problem which enjoys theoretical soundness. To utilize prior knowledge from query logs, we propose to use Bayesian inference and Thompson Sampling to solve this MAB problem. Extensive experiments on large scale datasets show that our QAC algorithm has the capacity to adaptively learn temporal trends, and outperforms existing QAC algorithms in ranking qualities.

Learning From Web Videos for Event Classification

Traditional approaches for classifying event videos rely on a manually curated training data set. While this paradigm has achieved excellent results on benchmarks, such as TrecVid multimedia event detection (MED) challenge data sets, it is restricted by the effort involved in careful annotation. Recent approaches have attempted to address the need for annotation by automatically extracting images from the web, or generating queries to retrieve videos. In the former case, they fail to exploit additional cues provided by video data, while in the latter, they still require some manual annotation to generate relevant queries. We take an alternate approach in this paper, leveraging the synergy between visual video data and the associated textual metadata, to learn event classifiers without manually annotating any videos. Specifically, we first collect a video data set with queries constructed automatically from textual description of events, prune irrelevant videos with text and video data, and then learn the corresponding event classifiers. We evaluate this approach in the challenging setting where no manually annotated training set is available, i.e., EK0 in the TrecVid challenge, and show state-of-the-art results on MED 2011 and 2013 data sets.

Multi-objective parametric query optimization

We propose a generalization of the classical database query optimization problem: multi-objective parametric query (MPQ) optimization. MPQ compares alternative processing plans according to multiple execution cost metrics. It also models missing pieces of information on which plan costs depend upon as parameters. Both features are crucial to model query processing on modern data processing platforms. MPQ generalizes previously proposed query optimization variants, such as multi-objective query optimization, parametric query optimization, and traditional query optimization. We show, however, that the MPQ problem has different properties than prior variants and solving it requires novel methods. We present an algorithm that solves the MPQ problem and finds, for a given query, the set of all relevant query plans. This set contains all plans that realize optimal execution cost tradeoffs for any combination of parameter values. Our algorithm is based on dynamic programming and recursively constructs relevant query plans by combining relevant plans for query parts. We assume that all plan execution cost functions are piecewise-linear in the parameters. We use linear programming to compare alternative plans and to identify plans that are not relevant. We present a complexity analysis of our algorithm and experimentally evaluate its performance.

Medical diagnoses using three-way fuzzy concept lattice and their Euclidean distance

Searching the closest symptoms of a particular disease in the given patient is a major concern for medical diagnoses expert. In most cases, this problem arises due to large number of incomplete, uncertain, or inconsistent information generated in medical diagnoses data set. Recently, some of the researchers tried to pay attention towards precise measurement of uncertainty and vagueness in medical data set using the properties of applied abstract algebra. In this process, researchers noticed that calculus of three-way decision space is more useful to characterize the uncertainty and vagueness in medical data set based on truth, indeterminacy, and falsity membership-values. However, searching some relevant query based on user requirement to diagnoses the disease is a major issue in three-way decision space. To encounter this problem, current paper tried to analyze the medical data set using the properties of single-valued neutrosophic graph-based concept lattice. In addition, another method is proposed to select some of the interesting three-way fuzzy concepts at user-defined granulation for their computed Euclidean distance with an illustrative example.

Hierarchical Contextual Attention Recurrent Neural Network for Map Query Suggestion

The query logs from an on-line map query system provide rich cues to understand the behaviors of human crowds. With the growing ability of collecting large scale query logs, the query suggestion has been a topic of recent interest. In general, query suggestion aims at recommending a list of relevant queries w.r.t. users' inputs via an appropriate learning of crowds' query logs. In this paper, we are particularly interested in map query suggestions (e.g., the predictions of location-related queries) and propose a novel model Hierarchical Contextual Attention Recurrent Neural Network (HCAR-NN) for map query suggestion in an encoding-decoding manner. Given crowds map query logs, our proposed HCAR-NN not only learns the local temporal correlation among map queries in a query session (e.g., queries in a short-term interval are relevant to accomplish a search mission), but also captures the global longer range contextual dependencies among map query sessions in query logs (e.g., how a sequence of queries within a short-term interval has an influence on another sequence of queries). We evaluate our approach over millions of queries from a commercial search engine (i.e., Baidu Map). Experimental results show that the proposed approach provides significant performance improvements over the competitive existing methods in terms of classical metrics (i.e., Recall@K and MRR) as well as the prediction of crowds' search missions.

The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries

This paper presents a method for combining query-relevance with information-novelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce redundancy while maintaining query relevance in re-ranking retrieved documents and in selecting apprw priate passages for text summarization. Preliminary results indicate some benefits for MMR diversity ranking in document retrieval and in single document summarization. The latter are borne out by the recent results of the SUMMAC conference in the evaluation of summarization systems. However, the clearest advantage is demonstrated in constructing non-redundant multi-document summaries, where MMR results are clearly superior to non-MMR passage selection.

Document Clustering Approach for Meta Search Engine

The size of WWW is growing exponentially with ever change in technology. This results in huge amount of information with long list of URLs. Manually it is not possible to visit each page individually. So, if the page ranking algorithms are used properly then user search space can be restricted up to some pages of searched results. But available literatures show that no single search system can provide qualitative results from all the domains. This paper provides solution to this problem by introducing a new meta search engine that determine the relevancy of query corresponding to web page and cluster the results accordingly. The proposed approach reduces the user efforts, improves the quality of results and performance of the meta search engine.

Menopause and big data: Word Adjacency Graph modeling of menopause-related ChaCha data.

To detect and visualize salient queries about menopause using Big Data from ChaCha. We used Word Adjacency Graph (WAG) modeling to detect clusters and visualize the range of menopause-related topics and their mutual proximity. The subset of relevant queries was fully modeled. We split each query into token words (ie, meaningful words and phrases) and removed stopwords (ie, not meaningful functional words). The remaining words were considered in sequence to build summary tables of words and two and three-word phrases. Phrases occurring at least 10 times were used to build a network graph model that was iteratively refined by observing and removing clusters of unrelated content. We identified two menopause-related subsets of queries by searching for questions containing menopause and menopause-related terms (eg, climacteric, hot flashes, night sweats, hormone replacement). The first contained 263,363 queries from individuals aged 13 and older and the second contained 5,892 queries from women aged 40 to 62 years. In the first set, we identified 12 topic clusters: 6 relevant to menopause and 6 less relevant. In the second set, we identified 15 topic clusters: 11 relevant to menopause and 4 less relevant. Queries about hormones were pervasive within both WAG models. Many of the queries reflected low literacy levels and/or feelings of embarrassment. We modeled menopause-related queries posed by ChaCha users between 2009 and 2012. ChaCha data may be used on its own or in combination with other Big Data sources to identify patient-driven educational needs and create patient-centered interventions.

An Efficient Indexing Method for Skyline Computations with Partially Ordered Domains

Efficient processing of skyline queries with partially ordered domains has been intensively addressed in recent years. To further reduce the query processing time to support high-responsive applications, the skyline queries that were previously processed with user preferences similar to those of the new query contribute useful candidate result points. Hence, the answered queries can be cached with both their results and the user preferences such that the query processor can rapidly retrieve the result for a new query only from the result sets of cached queries with compatible user preferences. When caching a significant number of queries accumulated over time, it is essential to adopt effective access methods to index the cached queries to retrieve a set of relevant cached queries for facilitating the cache-based skyline query computations. In this paper, we propose an extended depth-first search indexing method (e-DFS for short) for accessing user preference profiles represented by directed acyclic graphs (DAGs), and emphasize the design of the e-DFS encoding that effectively encodes a user preference profile into a low-dimensional feature point which is eventually indexed by an R-tree. We obtain one or more traversal orders for each node in a DAG by traversing it through a modified version of the depth-first search which is utilized to examine the topology structure and dominance relations to measure closeness or similarity. As a result, e-DFS which combines the criteria of similarity evaluation is able to greatly reduce the search space by filtering out most of the irrelevant cached queries such that the query processor can avoid accessing the entire data set to compute the query results. Extensive experiments are presented to demonstrate the performance and utility of our indexing method, which outperforms the baseline planning techniques by reducing 37 percent of the computational time on average.

Producción científica sobre cine indexada en la Web of Science en la categoría « Film, Radio & Television »: 2000-2016

El presente trabajo trata de analizar la producción científica contenida en la Web of Science en el periodo 2000-2016 sobre cine, publicada en fuentes indizadas bajo la categoría de Film, Radio, Television. Tras realizar las consultas pertinentes en la base de datos mencionada, se extraen un total de 12.514 trabajos, que son estudiados para analizar datos tales como número anual de documentos, países de afiliación de sus autores, categorías temáticas relacionadas, fuentes en las que se difunden los trabajos, idiomas de publicación, tipología documental y promedio de citas por documento.

Rapid Adjustment of Clinical Decision Support in Response to Updated Recommendations for Palivizumab Eligibility

SummaryBackground: Palivizumab is effective at reducing hospitalizations due to respiratory syncytial virus among high-risk children, but is indicated for a small population. Identification of patients eligible to receive palivizumab is labor-intensive and error-prone. To support patient identification we developed Clinical Decision Support (CDS) based on published recommendations in 2012. This CDS was developed using a systematic process, which directly linked computer code to a recommendation’s narrative text. In 2014, updated recommendations were published, which changed several key criteria used to determine eligible patients.Objective: Assess the effort required to update CDS in response to new palivizumab recommendations and identify factors that impacted these efforts.Methods: We reviewed the updated American Academy of Pediatrics (AAP) policy statement from Aug 2014 and identified areas of divergence from the prior publication. We modified the CDS to account for each difference. We recorded time spent on each activity to approximate the total effort required to update the CDS.Results: Of the 15 recommendations in the initial policy statement, 7 required updating. The CDS update was completed in 11 person-hours. Comparison of old and new recommendations was facilitated by the AAP policy statement structure and required 3 hours. Validation of the revised logic required 2 hours by a clinical domain expert. An informaticist required 3 hours to update and test the CDS. This included adding 24 lines and deleting 37 lines of code. Updating relevant data queries took an additional 3 hours and involved 10 edits.Conclusion: We quickly adapted CDS in response to changes in recommendations for palivizumab administration. The consistent AAP policy statement structure and the link we developed between these statements and the CDS rules facilitated our efforts. We recommend that CDS implementers establish linkages between published narrative recommendations and their executable rules to facilitate maintenance efforts.Citation: Michel J, Utidjian LH, Karavite D, Hogan A, Ramos MJ, Miller J, Shiffman RN, Grundmeier RW. Rapid adjustment of clinical decision support in response to updated recommendations for palivizumab eligibility. Appl Clin Inform 2017; 8: 581–592 https://doi.org/10.4338/ACI-2016-10-RA-0173

Spatial and Financial Aspects of a Protected Area Functioning – The Case of the Gorce National Park

Abstract Subject and purpose of work: The Gorce National Park (GNP) represents the object of the study. The purpose of the article is to present the GNP in the category of an area and in the category of an active managing unit. Materials and methods: Empirical research was conducted (2008-2014) based on the data collected from the GNP financial and accounting records and the Central Statistical Office. The degree of tourism function, carried out by the territorial municipalities connected with the GNP, was studied (Baretje and Defert index and Charvat index were calculated) and also the analysis of the GNP activities was conducted. Both the relevant literature query and the analysis of development strategies prepared by the territorial municipalities connected with the GNP were conducted. Results: It was found that the tourist function constitutes an important, however, not the primary function for the analysed municipalities, whereas the initiated activities (both on the part of municipalities and the GNP) indicate the desire for its intensification. The GNP has assets exceeding the value of PLN 17 million, offers jobs for the team whose total annual remuneration is almost PLN 2,5 million and the total annual costs for the last audited year amount to PLN 7,5 million. Conclusions: The described protected area constitutes the example of proper coexistence of human economic activity and nature. The GNP remains a significant entity of the local economic life scene which increases tourist attractiveness of the Gorce Mountains.

Investigating queries and search failures in academic search

Academic search concerns the retrieval and profiling of information objects in the domain of academic research. In this paper we reveal important observations of academic search queries, and provide an algorithmic solution to address a type of failure during search sessions: null queries. We start by providing a general characterization of academic search queries, by analyzing a large-scale transaction log of a leading academic search engine. Unlike previous small-scale analyses of academic search queries, we find important differences with query characteristics known from web search. E.g., in academic search there is a substantially bigger proportion of entity queries, and a heavier tail in query length distribution. We then focus on search failures and, in particular, on null queries that lead to an empty search engine result page, on null sessions that contain such null queries, and on users who are prone to issue null queries. In academic search approximately 1 in 10 queries is a null query, and 25% of the sessions contain a null query. They appear in different types of search sessions, and prevent users from achieving their search goal. To address the high rate of null queries in academic search, we consider the task of providing query suggestions. Specifically we focus on a highly frequent query type: non-boolean informational queries. To this end we need to overcome query sparsity and make effective use of session information.We find that using entities helps to surface more relevant query suggestions in the face of query sparsity. We also find that query suggestions should be conditioned on the type of session in which they are offered to be more effective. After casting the session classification problem as a multi-label classification problem, we generate session-conditional query suggestions based on predicted session type. We find that this session-conditional method leads to significant improvements over a generic query suggestion method. Personalization yields very little further improvements over session-conditional query suggestions.

Car-pedestrian and car-cyclist accidents in Hungary

Abstract This study is connected to the PROSPECT project (PROactive Safety for PEdestrians and CyclisTs) that has a goal of developing a proactive on-board-system to cars. This system will more effectively identify and manage the incidents with VRUs (Vulnerable Road User) compared to those that are currently on the market. One of the first steps of our task is to define with statistical examination the main causes and circumstances which lead to car-pedestrian and car-cyclist accidents in Hungary. The basis of the accident research was a database about raw accident data (in chart form) provided by the Hungarian Central Statistical Office (KSH). This data was then systematized by a data managing system, and after the relevant queries, we processed it. This paper presents the results of this investigation which will be used to improve the software and hardware of the on-board-system.

An ontology-based intelligent data query system in manufacturing networks

This paper investigates the development of an intelligent data query framework through the use of semantic web technologies for manufacturing purposes. The primary objectives of the ontology-based data query were to develop an efficient and scalable data interoperability and retrieval system; in order to find the most relevant query results with minimum message cost, most hits per query and least response time. This document explains the idea of ontology and the application of the same in the manufacturing domain. A computer simulation software was developed based on a real case study of distributed networks of manufacturing workshops. In this research, a semantic query algorithm was developed where query results are returned by investigating the semantic richness of each workshop. Results were compared with a semantic-free search mechanism based on key performance indicators. The results show the validity of the proposed model for efficient data query when compared to random search.

A new ontology ranking method with OntoDUIA for ontology retrieval system

Ontology is being widely used for data integration and knowledge discovery in the field of data engineering. Ontology ranking is one of the important functions of ontology search engine, which ranks the searched ontologies in a reasonable order. A good ranking method can help a user acquiring the satisfied ontology efficiently from a considerable amount of search results. However, existing methods in literatures are unable to rank ontologies to well meet user's ranking demand due to their inherent shortcomings. In this paper, a novel ontology ranking method OntoDUIA is proposed and presented. It evaluates user-query related ontologies and ranks them based on a set of metrics of query relevance, usability, instance distribution and authority of ontology. To evaluate OntoDUIA, a series of experiments are conducted to compare the performance among OntoDUIA and some existing ontology ranking methods as well as human experts. Experimental results show that OntoDUIA can effectively meet user's ontology ranking demand, and it achieves stable and reliable ranking results. Finally, OntoDUIA can also be applied to ontology retrieval system.

On the impact of domain expertise on query formulation, relevance assessment and retrieval performance in clinical settings

The large volumes of medical information available on the web may provide answers for a wide range of users attempting to solve health-related problems. While experts generally utilize reliable resources for diagnosis search and professional development, novices utilize different (social) web resources to obtain information that helps them manage their health or the health of people who they care for. A diverse number of related search topics address clinical diagnosis, advice searching, information sharing, connecting with experts, etc. This paper focuses on the extent to which expertise can impact clinical query formulation, document relevance assessment and retrieval performance in the context of tailoring retrieval models and systems to experts vs. non-experts. The results show that medical domain expertise 1) plays an important role in the lexical representations of information needs; 2) significantly influences the perception of relevance even among users with similar levels of expertise and 3) reinforces the idea that a single ground truth does not exist, thereby leading to the variability of system rankings with respect to the level of user’s expertise. The findings of this study presents opportunities for the design of personalized health-related IR systems, but also for providing insights about the evaluation of such systems.

Sketch-based manga retrieval using manga109 dataset

Manga (Japanese comics) are popular worldwide. However, current e-manga archives offer very limited search support, i.e., keyword-based search by title or author. To make the manga search experience more intuitive, efficient, and enjoyable, we propose a manga-specific image retrieval system. The proposed system consists of efficient margin labeling, edge orientation histogram feature description with screen tone removal, and approximate nearest-neighbor search using product quantization. For querying, the system provides a sketch-based interface. Based on the interface, two interactive reranking schemes are presented: relevance feedback and query retouch. For evaluation, we built a novel dataset of manga images, Manga109, which consists of 109 comic books of 21,142 pages drawn by professional manga artists. To the best of our knowledge, Manga109 is currently the biggest dataset of manga images available for research. Experimental results showed that the proposed framework is efficient and scalable (70 ms from 21,142 pages using a single computer with 204 MB RAM).

Efficient Processing of Relevant Nearest-Neighbor Queries

Novel Web technologies and resulting applications have led to a participatory data ecosystem that, when utilized properly, will lead to more rewarding services. In this work, we investigate the case of Location-Based Services, specifically how to improve the typical location-based Point-of-Interest (POI) request processed as a k -Nearest-Neighbor query. This work introduces Links-of-Interest (LOI) between POIs as a means to increase the relevance and overall result quality of such queries. By analyzing user-contributed content in the form of travel blogs, we establish the overall popularity of an LOI, that is, how frequently the respective POI pair was visited and is mentioned in the same context. Our contribution is a query-processing method for so-called k -Relevant Nearest Neighbor ( k -RNN) queries that considers spatial proximity in combination with LOI information to retrieve close-by and relevant (as judged by the crowd) POIs. Our method is based on intelligently combining indices for spatial data (a spatial grid) and for relevance data (a graph) during query processing. Using landmarks as a means to prune the search space in the Relevance Graph, we improve the proposed methods. Using in addition A*-directed search, the query performance can be further improved. An experimental evaluation using real and synthetic data establishes that our approach efficiently solves the k -RNN problem.