Efficient transaction support for dynamic information retrieval systems

A new approach for fuzzy information retrieval based on weighted power-mean averaging operators

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.

Humans have used electronic information retrieval (IR) systems for more than 50 years as they evolved from experimental systems to full-scale Web search engines and digital libraries. The fields of library and information science (LIS), cognitive science, human factors and computer science have historically been the leading disciplines in conducting research that seeks to model human interaction with IR systems for all kinds of information related behaviors. As technology problems have been mastered, the theoretical and applied framework for studying human interaction with IR systems has evolved from systems-centered to more user-centered, or cognitive-centered approaches. However, cognitive information retrieval (CIR) research that focuses on user interaction with IR systems is still largely under-funded and is often not included at computing and systems design oriented conferences. But CIR-focused research continues, and there are signs that some IR systems designers in academia and the Web search business are realizing that user behavior research can provide valuable insights into systems design and evaluation. The goal of our book is to provide an overview of new CIR research directions. This book does not provide a history of the research field of CIR. Instead, the book confronts new ways of looking at the human information condition with regard to our increasing need to interact with IR systems. The need has grown due to a number of factors, including the increased importance of information to more people in this information age. Also, IR was once considered document-oriented, but has now evolved to include multimedia, text, and other information objects. As a result, IR systems and their complexity have proliferated as users and user purposes for using them have also proliferated. Human interaction with IR systems can often be frustrating as people often lack an understanding of IR system functionality. New more holistic directions in CIR are emerging that conceptualize human-IR system interaction at the human-computer interaction level (taking into account interface issues), the information behavior level (taking into account the role of IR system interaction in the total range of people’s information behaviors), and the

Both phrases and Boolean queries have a long history in information retrieval, particularly in commercial systems. In previous work, Boolean queries have been used as a source of phrases for a statistical retrieval model, This work, like the majority of research on phrases, resulted in little improvement in retrieval effectiveness, In this paper, we describe an approach where phrases identified in natural language queries are used to build structured queries for a probabilistic retrieval model. Our results show that using phrases in this way can improve performance, and that phrases that are automatically extracted from a natural language query perform nearly as well as manually selected phrases.

Information Retrieval (IR) systems aim to retrieve data that satisfies certain requirements and constitute an important service in many types of networks, including Delay/Disruption Tolerant Networks (DTNs). In current DTN based IR systems, the data that satisfies a query is assumed to be stored on a single node. Therefore, once a node receives a query in which it has the corresponding data, the query can be resolved completely. However, in scenarios where a query requires data from multiple nodes, these IR systems may fail. Henceforth, in this paper, we propose Distributed Data-Centric Information Retrieval (DDC-IR), a data centric IR system that supports all query types; e.g., continuous and complex. More importantly, it is designed specifically to operate in DTNs. It also incorporates a new packet, aka Query Reply Packet, that includes both a query and one or more replies. We show how this packet facilitates efficient query resolution and enables data centric routing. In addition, it uses caching so that nodes store popular queries that has the effect of speeding up query resolution. We have conducted an extensive simulation study to compare DDC-IR to state of the art IR systems using the popular Random Waypoint model and a trace-file containing student movements on a campus. The results show that DDC-IR is able to resolve 50 % more queries and has an 80 % lower buffer occupancy level than existing IR systems. We also tested DDC-IR in networks with varying sizes. For networks with 100 nodes, DDC-IR is able to resolve queries while current IR systems fail to resolve any queries. In particular, when the number of nodes increases, current IR systems fail to resolve any queries, whilst DDC-IR is able to resolve complex and continuous queries. The influence of the number of sub-queries on query resolution time is also studied. Specifically, when the number of sub-queries in a complex query increases from five to nine, DDC-IR uses 50 % more time to resolve a query. In comparison, prior IR systems fail to resolve any queries.

Concept analysis via rough set and AFS algebra

The proliferation of the World Wide Web has brought information retrieval (IR) techniques to the forefront of search technology. To the average computer user, “searching” now means using IR-based systems for finding information on the WWW or in other document collections. IR query evaluation methods and workloads differ significantly from those found in database systems. In this paper, we focus on three such differences. First, due to the inherent fuzziness of the natural language used in IR queries and documents, an additional degree of flexibility is permitted in evaluating queries. Second, IR query evaluation algorithms tend to have access patterns that cause problems for traditional buffer replacement policies. Third, IR search is often an iterative process, in which a query is repeatedly refined and resubmitted by the user. Based on these differences, we develop two complementary techniques to improve the efficiency of IR queries: 1) Buffer-aware query evaluation, which alters the query evaluation process based on the current contents of buffers; and 2) Ranking-aware buffer replacement, which incorporates knowledge of the query processing strategy into replacement decisions. In a detailed performance study we show that using either of these techniques yields significant performance benefits and that in many cases, combining them produces even further improvements.

Purpose – People often engage in different information-seeking strategies (ISSs) within a single information-seeking episode. A critical concern for the design of information retrieval (IR) systems is how to provide support for these different behaviors in a manner which searchers can easily understand, navigate and use, as they move from one ISS to another. The purpose of this paper is to describe a dialogue structure that was implemented in an experimental IR system, in order to address this concern. Design/methodology/approach – The authors conducted a user-centered experiment to evaluate the IR systems. Participants were asked to search for information on two different task types, with four different topics per task, in both the experimental system and a baseline system emulating state-of-the-art IR systems. The authors report here the results related explicitly to the use of the experimental system's dialogue structure. Findings – For one of the task types, most participants followed the search steps as predicted in the dialogue structures, and those who did so completed the task in fewer moves. For the other task type, predicted order of moves was often not followed, but participants again used fewer moves when following the predicted order. Results demonstrate that the dialogue structures the authors designed indeed support effective human information behavior patterns in a variety of ways, and that searchers can effectively use a system which changes to support different ISSs. Originality/value – This study shows that it is both possible and beneficial, to design an IR system which can support multiple ISSs, and that such a system can be understood and used successfully.

F. Wilfrid Lancaster has earned a reputation for greatness in the evaluation of information storage and retrieval systems. Many of his extensive contributions stem from his early experience with the National Library of Medicine (NLM) MEDLARS system. His evaluation of the MEDLARS Demand Search Service in 1966 and 1967 was an important landmark as one of the earliest evaluations of a computer-based retrieval system and as the first application of re-call and precision measures in a large, operational database setting. In 1971, his evaluation of the MEDLARS AIM-TWX system was an important study of early online systems and their direct use by end users. This paper summarizes Lancaster’s two major evaluations of the MEDLARS system, including the information environment at the time and their impact in the field of information science. Examples of Lancaster’s other evaluation work with information retrieval systems are provided, followed by discussion of the textbooks that grew out of his evaluation experience and expertise. The article closes with comments from current and former NLM staff regarding Lancaster’s time at NLM or his influence on their own career.

In modern information retrieval (IR) systems, scoring functions have been extensively adopted for sorting results. For a given document, the rank in sorted result lists with respect to hot searches can be considered as its influence. When a new document comes, can we use such IR systems to evaluate its influence before we insert it into the corpus? Such issue may not be solved very well by current IR systems with inverted indexes. In this paper, an influence measure based on documents’ global rank is proposed, and the inverted index structure has been extended by adding the position milestones for speeding up the ranking calculation. Moreover, a performance study using both real data and synthetic data verifies the effectiveness and the efficiency of our method.

In Information Retrieval it is well-known that the complexity of processing boolean queries depends on the size of the intermediate results, which could be huge (and are typically on disk) even though the size of the final result may be quite small. In the case of inverted files the most time consuming operation is the merging or intersection of the list of occurrences [1]. We propose, the Keyword tree (K-tree) and forest, efficient structures to handle boolean queries in keyword-based information retrieval. Extensive simulations show that K-tree is orders-of-magnitude faster (i.e., far fewer I/O's) for boolean queries than the usual approach of merging the lists of occurrences and incurs only a small overhead for single keyword queries. The K-tree can be efficiently parallelized as well. The construction cost of K-tree is comparable to the cost of building inverted files.

Cross language information retrieval (CLIR) is a retrieval process in which the user fires queries in one language to retrieve information from another (different) language. The diversity of information and language barriers are the serious issues for communication and cultural exchange across the world. To solve such barriers, Cross language information retrieval system, are nowadays in strong demand. CLIR is a subset of Information Retrieval (IR) system. Information Retrieval deals with finding useful information from a large collection of unstructured, structured and semi-structured data to a user query where the query is a set of keywords. Information Retrieval can be classified into different classes such as Monolingual information retrieval, Bi-Lingual Information Retrieval, Multilingual information retrieval and Cross language information retrieval. This paper focuses on the various IR variants and techniques used in CLIR system. Further, based on available literature, a number of challenges and issues in CLIR have been identified and discussed. It gives an overview of the advantages, limitations, tools available in CLIR research. It also describes new application areas of CLIR such as medical, multimedia, question answering system etc. The need for exploring and building more specialized information system that enable speakers of an Odia language to discover valuable information beyond linguistic and cultural barriers. This study is aimed at building an experimental CLIR system between one of the under-resourced language (i.e. Odia) and one of the most commonly used online language (i.e. English) in future.

Now finding relevant information is absolutely needed by users of the information. Actually, finding relevant information is more difficult than just looking for information. The presence information retrieval (IR) system is a system for automatically searching the relevant information to the user needs, which is expressed in the required query, and this query will be used as an input and processed by IR system and then will get relevant documents to the required query. One of methods of finding relevant information by query is term weighting by both query and document. The commonly used methods are the term weighting of local and global weighting, while the local term weighting algorithms that are used such as TF, TF logarithmic, binary TF and augmented TF and global weighting is IDF, IDFP, IDFB. In this research, besides of comparison of term weighting algorithms, it is also proposed new improvement in term weighting algorithms by adding global weight value that changes base 10 logarithmic to base 2 logarithmic and adds the number 1 in the global term weighting IDFP. Measurements result of this research are Precision, Recall and NIAP. In This research, the best result for precision and NAIP have been performed by new propose term weighting algorithms. After compare and analyze the result of the weighting algorithms. It is necessary to combine the weighting with semantic algorithms so that it will get better result.

The dominant existing routing strategies employed in peer-to-peer(P2P) based information retrieval(IR) systems are similarity-based approaches. In these approaches, agents depend on the content similarity between incoming queries and their direct neighboring agents to direct the distributed search sessions. However, such a heuristic is myopic in that the neighboring agents may not be connected to more relevant agents. In this paper, an online reinforcement-learning based approach is developed to take advantage of the dynamic run-time characteristics of P2P IR systems as represented by information about past search sessions. Specifically, agents maintain estimates on the downstream agents' abilities to provide relevant documents for incoming queries. These estimates are updated gradually by learning from the feedback information returned from previous search sessions. Based on this information, the agents derive corresponding routing policies. Thereafter, these agents route the queries based on the learned policies and update the estimates based on the new routing policies. Experimental results demonstrate that the learning algorithm improves considerably the routing performance on two test collection sets that have been used in a variety of distributed IR studies.

Ranking is likely the most important process of an information retrieval (IR) system that will be used to evaluate and measure the effectiveness of an IR system. This paper aims to produce the implementation of fuzzy logic controller of Mamdani-type fuzzy inference system for defining the ranking function by using the BM25 model in the Malay IR system that also includes the Malay Stemmer. The result of the ranking function then will be compared to the result of vector space model that is also applied in Malay IR system and be evaluated using relevant document by the hadith expert. The results showed that FBMIR has slightly outperformed vector space model on three topic set of query results such as 'Iman', 'Ilmu' and 'Wuduk' on the precision at rank 10 and the percentage of no relevant document in the top ten retrieved measures.