An efficient and scalable search engine for models

Search engines extract data from relevant sources and make them available to users via queries. A search engine typically crawls the web to gather data, analyses and indexes it and provides some query mechanism to obtain ranked results. There exist search engines for websites, images, code, etc., but the specific properties required to build a search engine for models have not been explored much. In the previous work, we presented MAR, a search engine for models which has been designed to support a query-by-example mechanism with fast response times and improved precision over simple text search engines. The goal of MAR is to assist developers in the task of finding relevant models. In this paper, we report new developments of MAR which are aimed at making it a useful and stable resource for the community. We present the crawling and analysis architecture with which we have processed about 600,000 models. The indexing process is now incremental and a new index for keyword-based search has been added. We have also added a web user interface intended to facilitate writing queries and exploring the results. Finally, we have evaluated the indexing times, the response time and search precision using different configurations. MAR has currently indexed over 500,000 valid models of different kinds, including Ecore meta-models, BPMN diagrams, UML models and Petri nets. MAR is available at http://mar-search.org.

Diving in Nose First: The Influence of Unfamiliar Search Scale and Environmental Context on the Search Performance of Volunteer Conservation Detection Dog–Handler Teams

Conservation detection dogs (CDDs) are trained to locate biological material from plants and animals of interest to conservation efforts and are often more effective and economical than other detection methods. However, the financial costs of developing and appropriately caring for CDDs can nonetheless prohibit their use, particularly by smaller conservation organizations. Training skilled volunteers to work with suitable pet dogs may help address this constraint. We sought to further develop the skills of 13 volunteer dog–handler teams that were trained in a previous study to detect myrrh essential oil in controlled laboratory conditions. We assessed search sensitivity, search effort, search precision and false-alert instances through progressive training stages increasing in size and environmental complexity. First, teams searched various-sized areas before and after 12 weeks of search training on a sports-field. Next, teams searched various-sized areas before and after seven weeks of training in bushland. Overall, search sensitivity decreased by approximately 20% in each unfamiliar context, compared to performance in familiar contexts. However, sensitivity typically improved from baseline performance by 10–20% after a period of training. Six teams found at least 78% of targets after training in bushland, yet sensitivity ranged from 29% to 86% between teams. We maintain that the foundational skills developed previously were necessary to prepare volunteer teams for field surveys involving conservation related targets. However, our results highlight the need to also train volunteer CDD teams in search scale and environmental contexts similar to their intended working conditions.

Semantically Enhanced Information Retrieval System

The user gives an input query in classical In-formation Retrieval (IR) system, keywords of the query are extracted and also the matching documents that contain one or more words specified by the user are retrieved. Keyword searches have a tricky time distinguishing between words that are spelled in similar way but mean something different. This often leads to hits that are completely irrelevant to the query. Se-mantic search seeks to enhance search precision by understanding searcher intent and along with the contextual significance of terms, as they seem within the searchable information space, whether on the net or within a closed system, to get more applicable outcomes. Semantically Enhanced Information Retrieval(SEIR) system can overcome the constraints of keyword based search. SEIR can semantically enhance the IR process. Therein way, searching is finished considering the meanings of query in-stead of the literal strings. Such a research automates tasks that need conceptual understanding of objects.

Improving retrieval of structured and unstructured information

Every day, information users struggle to find relevant documents and data needed to perform their jobs effectively and efficiently. This paper presents a strategic, practical approach to information architecture, focusing on developing and adding metadata to improve information retrieval. The review starts with a discussion of common information management problems and potential business benefits, and addresses the need for overarching principles and policies to be aligned with a comprehensive enterprise architecture. Next, the paper outlines a solution based on combining elements of information architecture – the hierarchy of a taxonomy, the synonyms from a thesaurus and the relationships in an ontology. It discusses how this hybrid structure can improve content classification, user navigation and enterprise search. The final sections will explain how taxonomy-generated rules can improve content classification and search, and present the results of a recent Proof of Concept (PoC) for automated content classification. The article also includes two sidebars – one offering taxonomy best practice guidelines and the other exploring an advanced classification weighting to improve search precision.

Similarity Digest Search: A Survey and Comparative Analysis of Strategies to Perform Known File Filtering Using Approximate Matching

Digital forensics is a branch of Computer Science aiming at investigating and analyzing electronic devices in the search for crime evidence. There are several ways to perform this search. Known File Filter (KFF) is one of them, where a list of interest objects is used to reduce/separate data for analysis. Holding a database of hashes of such objects, the examiner performs lookups for matches against the target device. However, due to limitations over hash functions (inability to detect similar objects), new methods have been designed, called approximate matching. This sort of function has interesting characteristics for KFF investigations but suffers mainly from high costs when dealing with huge data sets, as the search is usually done by brute force. To mitigate this problem, strategies have been developed to better perform lookups. In this paper, we present the state of the art of similarity digest search strategies, along with a detailed comparison involving several aspects, as time complexity, memory requirement, and search precision. Our results show that none of the approaches address at least these main aspects. Finally, we discuss future directions and present requirements for a new strategy aiming to fulfill current limitations.

An experimental search strategy retrieves more precise results than PubMed and Google for questions about medical interventions

Objective: To compare the precision of a search strategy designed specifically to retrieve randomized controlled trials (RCTs) and systematic reviews of RCTs with search strategies designed for broader purposes. Methods: We designed an experimental search strategy that automatically revised searches up to five times by using increasingly restrictive queries as long at least 50 citations were retrieved. We compared the ability of the experimental and alternative strategies to retrieve studies relevant to 312 test questions. The primary outcome, search precision, was defined for each strategy as the proportion of relevant, high quality citations among the first 50 citations retrieved. Results: The experimental strategy had the highest median precision (5.5%; interquartile range [IQR]: 0% - 12%) followed by the narrow strategy of the PubMed Clinical Queries (4.0%; IQR: 0% - 10%). The experimental strategy found the most high quality citations (median 2; IQR: 0 - 6) and was the strategy most likely to find at least one high quality citation (73% of searches; 95% confidence interval 68% - 78%). All comparisons were statistically significant. Conclusions: The experimental strategy performed the best in all outcomes although all strategies had low precision.

An experimental search strategy retrieves more precise results than PubMed and Google for questions about medical interventions

Objective: To compare the precision of a search strategy designed specifically to retrieve randomized controlled trials (RCTs) and systematic reviews of RCTs with search strategies designed for broader purposes. Methods: We designed an experimental search strategy that automatically revised searches up to five times by using increasingly restrictive queries as long at least 50 citations were retrieved. We compared the ability of the experimental and alternative strategies to retrieve studies relevant to 312 test questions. The primary outcome, search precision, was defined for each strategy as the proportion of relevant, high quality citations among the first 50 citations retrieved. Results: The experimental strategy had the highest median precision (5.5%; interquartile range [IQR]: 0% - 12%) followed by the narrow strategy of the PubMed Clinical Queries (4.0%; IQR: 0% - 10%). The experimental strategy found the most high quality citations (median 2; IQR: 0 - 6) and was the strategy most likely to find at least one high quality citation (73% of searches; 95% confidence interval 68% - 78%). All comparisons were statistically significant. Conclusions: The experimental strategy performed the best in all outcomes although all strategies had low precision.