Ranking Of Search Results Research Articles

Abstract 1284: How can you interpret gene lists from -omics experiments

Abstract Understanding the mechanisms responsible for a cellular behaviour often begins with observations of genes and gene products. Depending on the type of experiment, the number of resulting genes can be small, but increasingly, researchers are faced with many thousands of measurements, as in the case of transcriptomic or protein-DNA binding observations. Here, we describe ways to pair experimental results consisting of one or more genes with analysis tools with the overall aim being to make results more biologically interpretable. In certain cases, experimental approaches such as screens for essential genes can generate one or a few ‘genes of interest' and there is a desire to understand their relationship to one another as well as discover links to additional, interesting genes. To this end, ‘GeneMANIA' is a web tool that accepts gene names and returns a network visualization of related genes based on similarity in expression, localization, protein domains and those involved in physical interactions. Likewise, ‘PCViz' is a web tool that displays a network of interactions drawn from Pathway Commons, a web resource for pathway and interaction knowledge. In cases where experiments generate a lengthy list of genes, for instance, transcriptomic measurements, there is a desire to understand their relevance to a phenotype of interest. Pathway enrichment analysis methods aim to summarize gene lists as pathways, which have a closer link to cell function. An online ‘Guide' by Pathway Commons includes workflows that illustrate how to chain together software tools to identify pathways from the corresponding gene-level data then organize and summarize the pathway-level results in an interactive visualization known as an Enrichment Map. For those wishing to drill-down to individual pathways, Pathway Commons offers a set of web apps, including ‘Search' that enables users to query by keyword and visualize ranked search results. Ongoing development of web apps aims to enhance the accessibility to pathways and integrate support for analysis and visualization of experimental data. The full complement of data, tools and resources offered by Pathway Commons in support of pathway analysis are described. Citation Format: Jeffrey V. Wong, Augustin Luna, Emek Demir, Igor Rodchenkov, Özgün Babur, Chris Sander, Gary D. Bader. How can you interpret gene lists from -omics experiments [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1284.

Partnership of Digital Libraries Hathi Trust Digital Library: the New Mode of Cooperation

There is analysed the activities of digital libraries partnership Hathi Trust Digital Library (HTDL), arose in October 2008 in the United States as a collaborative repository of digital collections of library systems of 13 universities in the U.S. Middle West and digital collections of 11 universities in California (now numbering 124 libraries). In the United States — the leader of IT-technologies — has been accumulated the vast experience in the field of organization of electronic resources, carried out a large-scale reorganization of library industry, the results of which are not sufficiently covered in the domestic library science. For the first time, the Russian library community is invited to learn the specific experience of the formation and operation of the partnership of digital libraries HTDL, based on the principle of institutional cooperation and coordination of management solutions in the conservation and maintenance of electronic collections.The main tasks of the distributed data warehouse belonging to the partnership group of large research libraries are: providing access to the digital collection first of all for collective users, libraries — co-founders; preservation of intellectual heritage through reliable and accessible electronic representation, improvement of open technological infrastructure. The HTML concept is based on the conscious preservation of the specificity of research libraries, when the user is offered a body of special catalogued literature, and the ranking of search results is not influenced by the commercial interests. It is noted that HTDL is built on the basis of an extensive network of interlibrary entities in the United States with extensive cooperation in the field of administration, cataloguing, and storage of printed materials.There are considered the priority achievements of Hathi Trust Digital Library: development of the new models of interlibrary cooperation, efficient distributed management structure, transparent scheme of payment calculation of infrastructure maintenance. The dynamics of the development of this resource from the moment of formation to the present time is analysed.

Improving file locality in multi-keyword top-k search based on clustering

Nowadays, fast growing number of users and business are motivated to outsource their private data to public cloud servers. Taking into consideration security issues, private data should be encrypted before being outsourced to remote servers, though this makes traditional plaintext keyword search rather difficult. For this reason, there exists an urgent need of an efficient and secure searchable encryption technology. In this paper, an affinity propagation (AP) K-means clustering method (CAK-means, a combination of AP and K-means clustering) is proposed to realize fast searchable encryption in Big Data environments. CAK-means clustering utilizes affinity propagation to initialize K-means clustering, thereby making the clustering process faster, stable and effectively improving the initial clustering center quality of the K-means. As the AP algorithm identifies the clustering center with much lower errors than other methods, it significantly improves the search accuracy. Simultaneously, the related files in one cluster are stored at the contiguous locality of disks which will substantially improve the file locality and speedup the read and write disk I/O. Additionally, the coordinated matching measure is utilized to support accurate ranking of search results. Experimental results show that the proposed CAK-means-based multi-keyword ranked searchable encryption scheme (MRSE-CAK) has higher search efficiency and accuracy while simultaneously ensuring equivalent security.

To Brush or Not to Brush: Product Rankings, Customer Search, and Fake Orders

“Brushing---the practice of online merchants placing fake orders of their own products to artificially inflate sales on e-commerce platforms---has recently received widespread public attention. On the one hand, brushing enables merchants to boost their rankings in search results, because products with higher sales volume are often ranked higher. On the other hand, rankings matter because search frictions faced by customers narrow their attention to only the few products that show up at the top. Thus, fake orders from brushing may affect customer choice. We build a stylized model to understand merchants’ strategic brushing behavior and its welfare implications. We consider two competing merchants selling substitutable products (one of high quality, the other of low quality) in an evolutionary sales-based ranking system that assigns a higher ranking to a product with higher sales. In principle, such an adaptive system improves customer welfare relative to a case in which products are randomly ranked, but it also triggers brushing as an unintended consequence. Since the high-quality merchant receives a favorable bias in the sales-based ranking, he mainly has a defensive brushing incentive, whereas the low-quality merchant mostly has an offensive brushing incentive. As a result, brushing is a double-edged sword for customers. It may lead customer welfare to be even lower than what it would be in a random-ranking system, but in some other cases, it can surprisingly improve customer welfare. If brushing is more difficult for merchants (e.g., due to tougher regulations), it may make customers worse off as it attenuates brushing by the high-quality merchant but induces the low-quality one to brush more aggressively. If search is easier for customers (e.g., due to improved search technologies), it can actually hurt them as it may disproportionately discourage the high-quality merchant from brushing.

An Efficient Ranked Multi-Keyword Search for Multiple Data Owners Over Encrypted Cloud Data

With the development of cloud storage, more data owners are inclined to outsource their data to cloud services. For privacy concerns, sensitive data should be encrypted before outsourcing. There are various searchable encryption schemes to ensure data availability. However, the existing search schemes pay little attention to the efficiency of data users' queries, especially for the multi-owner scenario. In this paper, we proposed a tree-based ranked multi-keyword search scheme for multiple data owners. Specifically, by considering a large amount of data in the cloud, we utilize the TF × IDF model to develop a multikeyword search and return the top-k ranked search results. To enable the cloud servers to perform a secure search without knowing any sensitive data (e.g., keywords and trapdoors), we construct a novel privacypreserving search protocol based on the bilinear mapping. To achieve an efficient search, for each data owner, a tree-based index encrypted with an additive order and privacy-preserving function family is constructed. The cloud server can then merge these indexes effectively, using the depth-first search algorithm to find the corresponding files. Finally, the rigorous security analysis proves that our scheme is secure, and the performance analysis demonstrates its efficacy and efficiency.

Effects of task complexity on online search behavior of adolescents

Evaluation of information during information problem‐solving processes already starts when trying to select the appropriate search result on a search engine results page (SERP). Up to now, research has mainly focused on the evaluation of webpages, while the evaluation of SERPs received less attention. Furthermore, task complexity is often not taken into account. A within‐subjects design was used to study the influence of task complexity on search query formulation, evaluation of search results, and task performance. Three search tasks were used: a fact‐finding, cause–effect, and a controversial topic task. To measure perceptual search processes, we used a combination of log files, eye‐tracking data, answer forms, and think‐aloud protocols. The results reveal that an increase in task complexity results in more search queries and used keywords, more time to formulate search queries, and more considered search results on the SERPs. Furthermore, higher ranked search results were considered more often than lower ranked results. However, not all the results for the most complex task were in line with expectations. These conflicting results can be explained by a lack of prior knowledge and the possible interference of prior attitudes.

Privacy-preserving ranked neighbor query over encrypted graph data in the cloud

As cloud computing becomes prevalent, many data owners outsource their large number of graph data into the cloud for cost savings. The cloud may not be fully trusted and usually likes to learn owners' data. To protect the security of graph data, sensitive data have to be stored in an encrypted form. Neighbor queries are the most essential operations, many other operations can be built based on the neighbor queries. Ranked neighbor queries can make the queries very flexible, a query user can get ranked search results. When the graph data are encrypted, the operations over graph data are very inconvenient. Ranked neighbor queries over encrypted graph data are difficult to directly achieve. In this paper, we propose a solution to perform privacy-preserving ranked neighbor query over encrypted graph data in the cloud, which is named as PRNQ. Our work utilizes ranking function and encryption mechanism to perform ranked neighbor query. The query users can obtain the most wanted search results by this solution. Our paper proposes a design scheme for the weighted directed graph and proves the security and correctness. Experimental results with a real graph data set demonstrate the validity and efficiency of our scheme. Copyright © 2016 John Wiley & Sons, Ltd.

Accurate and efficient query clustering via top ranked search results

Accurate and efficient query clustering via top ranked search results

How to Glean Culture from an Evolving Internet: Richard Rogers, Digital Methods

How to Glean Culture from an Evolving InternetRichard Rogers, Digital Methods William Thomas (bio) Methodological discussion among historians of science and technology typically manifests as semiphilosophical reflection on the means by which knowledge and technologies are produced, and the ways in which they interact with politics and culture. The idea is that the proper interpretation of sources and the development of proper narratives hinge on a sound understanding of such issues. By comparison, only fleeting attention is generally paid to more technical questions, such as how historical documents are produced and accessed. Richard Rogers’s Digital Methods (Cambridge, MA: MIT Press, 2013. Pp. 275. $42) is about exactly that type of question. Rogers, who holds a chair in new media and digital culture at the University of Amsterdam, defines the term “digital methods” in a fairly strict way. First, the concept is not to be conflated with the “digital humanities.” Digital methods are not about using computers and the Internet to analyze and present work; they are about mining the Internet for source material for social scientific and humanistic analysis. Second, digital methods pertain to content that is born digital, and so do not encompass things like the digitized library holdings found in Google Books. Thus, Digital Methods will mainly interest sociologists, anthropologists, and historians studying history from the 1990s to the present. Finally, Rogers is not interested in online culture as a peculiar form of culture (i.e., cyberculture). He is interested in how we can use online sources to study culture generally, much as a historian might ponder how culture can and cannot be perceived in sources such as historical cinema or preserved correspondence. Yet Digital Methods is not about anthropological methodology. Rogers [End Page 238] does not spend much time discussing what kinds of voices are and are not to be found in various online locations. There is also no discussion of what online texts might or might not reveal about the broader systems of ideas held by the people who produced those texts. Digital Methods is more like a manual intended for people from a variety of disciplines who may wish to start using digital source material. Of course, one should note from Rogers’s examples of digital methods in action that he (and his students and colleagues) tend to use these methods to glean suggestive fragments of culture from the vast trove of online sources that exist. For instance, one key example involves using search engines within different national domains (e.g., .uk, .fr, .ar) to examine the different types of “rights” (e.g., human rights, children’s rights, cyclists’ rights) that appear in highly ranked search results. The idea is to gain insight into what sorts of rights are most prevalent in different national cultures. But what broader conclusions should we draw from such findings? That is beyond the scope of the book. Caveat emptor. Despite all the things that digital methods do not encompass, the concept does capture a diverse set of considerations. An early chapter describes how one can chart the ways that various sites do and do not link to each other, thereby revealing the “politics” of linking. This method can be used to gauge which organizations are regarded as authoritative in different communities, or how communities use links to draw on the prestige of more prevalent organizations and to promote less prevalent organizations, or how sites avoid linking to other sites to avoid, for instance, implying an endorsement of the site’s owners. The next chapter discusses how website archiving works (most notably through the Wayback Machine at archive.org) and how it fails to archive such content as comments, cookies, and advertisements. As advertisements from print media archives are presently considered a rich source for cultural analysis, such limitations should be taken seriously. Even so, examining the evolution of an organization’s website can provide important insights into changes in the organization’s declared priorities (reflected, for instance, in how items are presented on or removed from its website’s front page), into how it has responded to external events, and even into long-term cultural shifts reflected in trends such as an increasing use of radical rhetoric...

Multiple Databases are Needed to Search the Journal Literature on Computer Science

A Review of:
 Cavacini, A. (2015). What is the best database for computer science journal articles? Scientometrics 102(3): 2059-2071. http://dx.doi.org/10.1007/s11192-014-1506-1
 
 Objective – To compare the coverage of computer science literature in four bibliographic databases by checking the indexing of a selection of journal articles. The purpose of this comparison was to identify the most comprehensive database in computer science and determine whether more than one database is needed to search for articles on computer science topics. 
 
 Design – Comparative database evaluation using citation analysis.
 
 Setting – Computer science journal literature found within the INSPEC, Scopus, Web of Science, and DBLP databases.
 
 Subjects – 1,135 computer science journal articles published by an Italian university’s researchers from 1979 to 2014.
 
 Methods – The University of Milan’s institutional repository (AIR), containing publications authored by the university’s researchers, was searched in October 2014 for journal articles that were assigned the subject heading “informatica” (the word for computer science in Italian). The author then searched the titles of these journal articles in each of the databases to check whether they were indexed. For articles indexed in all four databases, the author also examined the quality of the bibliographic records by looking for the presence of 20 elements (e.g., the “cited by” option, ranking of search results, precision of results, etc.) in each database’s record. These overlapping articles were also searched in Google Scholar to help compare the quality of the records between the databases. 
 
 Main Results – Scopus indexed 75.86% of the journal articles found in AIR, Web of Science indexed 64.49%, DBLP indexed 61.15%, and INSPEC indexed 53.39%. Web of Science and INSPEC put together covered 74.80% of the articles, which is comparable to the amount indexed by Scopus. DBLP and Scopus contained the highest number of references to articles that were not found in the other databases, about 4% each. Out of the 1,135 journal articles, 391 (34.45%) were indexed by all four databases, with Web of Science scoring the highest for providing the best quality bibliographic records for these articles. 
 
 Conclusions – According to the author, the findings showed that INSPEC, Scopus, Web of Science, and DBLP “complemented each other, in a way that neither one could replace the other” (p. 2068) when searching the computer science literature. While there was overlap between databases, they each also contained unique articles.

Ranking Search Results in Library Information Systems — Considering Ranking Approaches Adapted From Web Search Engines

For an information retrieval system to be successful, it must have the ability to rank search results. As web search engines are the most often used and — in terms of ranking functionality — the most advanced existing systems, the principles they are based on and the strategies they use can be advantageous when applied to the library context. We categorize ranking factors into six different groups: 1. text statistics, 2. popularity, 3. freshness, 4. locality and availability, 5. content properties and 6. user background. We discuss the basic concepts and assumptions these ranking factors involve and offer potential implementations in the library context. The practice recommended here is for libraries to not only apply selected ranking factors — as existing library information systems already do — but to systematically test for the ranking factors best suited to their systems. We argue for a user-centric view on ranking, because in the end, ranking should be for the benefit of the user, and user preferences may vary across different contexts.

Effective and efficient similarity search in scientific workflow repositories

Scientific workflows have become a valuable tool for large-scale data processing and analysis. This has led to the creation of specialized online repositories to facilitate workflow sharing and reuse. Over time, these repositories have grown to sizes that call for advanced methods to support workflow discovery, in particular for similarity search. Effective similarity search requires both high quality algorithms for the comparison of scientific workflows and efficient strategies for indexing, searching, and ranking of search results. Yet, the graph structure of scientific workflows poses severe challenges to each of these steps. Here, we present a complete system for effective and efficient similarity search in scientific workflow repositories, based on the Layer Decomposition approach to scientific workflow comparison. Layer Decomposition specifically accounts for the directed dataflow underlying scientific workflows and, compared to other state-of-the-art methods, delivers best results for similarity search at comparably low runtimes. Stacking Layer Decomposition with even faster, structure-agnostic approaches allows us to use proven, off-the-shelf tools for workflow indexing to further reduce runtimes and scale similarity search to sizes of current repositories.

Click efficiency: a unified optimal ranking for online Ads and documents

Ranking of search results and ads has traditionally been studied separately. The probability ranking principle is commonly used to rank the search results while the ranking based on expected profits is commonly used for paid placement of ads. These rankings try to maximize the expected utilities based on the user click models. Recent empirical analysis on search engine logs suggests unified click models for both ranked ads and search results (documents). These new models consider parameters of (i) probability of the user abandoning browsing results (ii) perceived relevance of result snippets. However, current document and ad ranking methods do not consider these parameters. In this paper we propose a generalized ranking function--namely Click Efficiency (CE)--for documents and ads based on empirically proven user click models. The ranking considers parameters (i) and (ii) above, optimal and has the same time complexity as sorting. Furthermore, the CE ranking exploits the commonality of click models, hence is applicable for both documents and ads. We examine the reduced forms of CE ranking based upon different underlying assumptions, enumerating a hierarchy of ranking functions. Interestingly, some of the rankings in the hierarchy are currently used ad and document ranking functions; while others suggest new rankings. Thus, this hierarchy illustrates the relationships between different rankings, and clarifies the underlying assumptions. While optimality of ranking is sufficient for document ranking, applying CE ranking to ad auctions requires an appropriate pricing mechanism. We incorporate a second price based mechanism with the proposed ranking. Our analysis proves several desirable properties including revenue dominance over Vickrey Clarke Groves (VCG) for the same bid vector and existence of a Nash equilibrium in pure strategies. The equilibrium is socially optimal, and revenue equivalent to the truthful VCG equilibrium. As a result of its generality, the auction mechanism and the equilibrium reduces to the current mechanisms including Generalized Second Price Auction (GSP) and corresponding equilibria. Furthermore, we relax the independence assumption in CE ranking and analyze the diversity ranking problem. We show that optimal diversity ranking is NP-Hard in general, and a constant time approximation algorithm is not likely. Finally our simulations to quantify the amount of increase in different utility functions conform to the results, and suggest potentially significant increase in utilities.

The implementation of algorithm of ranking search result in information system of University library

This paper describes the implementation of the algorithm of ranking search results of the user in informational and search system of library. Today, in Ukraine almost no university libraries, that aren't computerized, at least partially. Libraries offer a range of remote services for their users – e-catalog and specialized e-services. If these services are provided by web means then access to them in most cases is anonymous. Today, libraries have large databases that describe millions of books, magazines and newspapers. If the user does not clearly imagine that he searches, then deal in such large arrays is very difficult. Therefore, in addition to specific features of information system that are used to refine the search query, an important element is the function of governing the presentation of search results - ranking. Implementation of ranking algorithms in the work of library information systems allow users to minimize the time required to find and organize information search based on qualitative criteria. In scientific and technical library of the National University Lviv Polytechnic was developed specialized information system that allows you to create a list of books for students. The proposed ranking algorithm was incorporated in the work of the system. In the opinion of users it is greatly improved process for selecting literature for the study of academic disciplines, if literature, recommended by the teacher, doesn't fit for some reasons

XML based Keyword Search

The success of information retrieval style keyword search on the web leads to the emergence of XML based keyword search. The text database and XML database differences leads to three new challenges: 1) The users search intention is to be identified, i.e., the XML node types that user wants to search for and search via is identified. 2) The similarities in tag name, tag value and the structure of tags are identified. 3) New scoring function is needed to estimate the output of the search results (XML document) relevance to the given query. However, these challenges cannot be addressed by the existing system, which results in low quality results in terms of query relevance. In this paper, an IR-style approach is proposed which basically utilizes the statistics of underlying XML data to address these challenges. First, specific guidelines that a search engine should meet in both search intention identification and relevance oriented ranking for search results is proposed. Then, based on these guidelines, a novel XML TF*IDF ranking strategy to rank the individual matches of all possible search intentions is proposed.

A Link-click-concept based Ranking Algorithm for Ranking Search Results

Personalized search is an essential research area that has main goal to determine the uncertainty of query terms. In order to enhance the relevance of search results, personalized search engines form user profiles which capture the users' personal preferences and by using those preferences find out the actual goal of the input query. By using User profile we can rank the documents in a search engine according to the query which is submitted by user. A better user profiling strategy is an important and primary component in search engine personalization. In this work, we propose a scheme that supports mining a user's conceptual preferences from users' click through data resulted from web search. This discovered preference is helpful to adapt a search engine's ranking function. In the system, an absolute set of conceptual preferences is derived for a user such as the concepts extracted from the search results and the click through data. After that, a Concept-based User Profile (CUP) in other words a concept ontology tree is generated. Our system proposes a novel approach such as Link-Click-Concept based Ranking Algorithm. The proposed system considers the concept for the user profile construction and has high efficiency than the existing system.

Calculating Page Pertinence for Web Referencing: A New Approach

Every day, more than three billion queries are performed on Google [1]. To take advantage of opportunities that you’re offered by the search engines, it is important for your website to achieve high rankings in search results, in order to reach a larger customer base, increasing its business in other regions (or countries). One goal of any business is to convert a maximum of Internet users into potential customers. ALGORITHM[2] links that allowed Google to be so successful and become what it is today! But how algorithm function to rank sites in terms of importance ? What formulas are the basis for website ranking ? The developers carry out tests and then draw conclusions. The purpose of this paper is to find a method (algorithm) which calculates the relevance of each page, we unveil parts of the code of the largest search engine in the world.

Monotonic qualitative logic programs: Computation and applications

A qualitative logic program QLP is a logic program where a dominance is associated with each rule in the program. The intuition is that rules with higher dominance are more plausible or more reliable. Literals in the answer sets of QLPs are also annotated with weights, with the intuition that a literal with a higher weight is more likely to be true. We present three different applications of QLPs: Ontology Matching, Ranking of search results and Inheritance Networks. We also address the problem of computing answer sets of QLPs. We define a property of QLPs called “monotonicity” and show that answer sets of monotonic QLPs can be computed in an “anytime” fashion, such that the literals are produced by a descending order of their dominance. We then present an application of QLP called LPmatch, which is a tool for ontology matching. LPmatch is a simple matcher composed of only nineteen logic programming rules. The most significant advantage of LPmatch is its flexibility as it can handle new domains for ontology matching instantly just by adding new rules. Comparisons with other existing tools show that LPmatch is a top performer.

Indexing method for effective web service retrieval

This paper addresses the problem of web service retrieval by presenting a new approach to indexing of both SOAP and RESTful web services. The approach uses index structure called parametric index that allows users to retrieve ranked results in accordance with specific parameters. The parameters refer to service's integral components and are covered in presented formal definition of a web service. Secondly, the services are modelled in vector space that allows the evaluation of their mutual relevance and enables obtaining ranked search results. In order to reduce the index size and to decrease the search time, presented approach uses the method of conceptual indexing which groups relevant service components into concepts. For further search performance improvement, this paper also introduces the concept of merged weight vector. The research is supported by novel indexing algorithm and implementation of proposed approach. Experimental results confirm that the presented approach significantly reduces the index size. It also improves search performance in contrast to standard indexing algorithm and compared to k-means and self-organising maps clustering algorithms.

Information Technology in the E-Commerce Marketing Strategy of OAWAY Website

In this paper, it is analyzing the e-commerce marketing strategy with information technology in the promotion process of SME (small and medium-sized enterprises) . Some marketing methods is indicated. There are through search engine optimization, microblogging marketing, BBS marketing, event marketing technology , etc. Using these methods, the e-commerce website can be faster and better indexed by search engines with information technology. Then, improve the e-commerce website's ranking in search results. Thus, enhance the e-commerce website's sales, corporate online image and corporate culture. This kind of marketing strategy is more suitable for long-term optimization and promotion of SME e-commerce website. The corporate e-commerce network marketing will bring better results.