Information access tools for software reuse

Software reuse has long been touted as an effective means to develop software products. But reuse technologies for software have not lived up to expectations. Among the barriers are high costs of building software repositories and the need for effective tools to help designers locate reusable software. Although many design-for-reuse and software classification efforts have been proposed, these methods are cost-intensive and cannot effectively take advantage of large stores of design artifacts that many development organizations have accumulated. Methods are needed that take advantage of these valuable resources in a cost-effective manner. This article describes an approach to the design of tools to help software designers build repositories of software components and locate potentially reusable software in those repositories. The approach is investigated with a retrieval tool, named CodeFinder, which supports the process of retrieving software components when information needs are ill-defined and users are not familiar with vocabulary used in the repository. CodeFinder uses an innovative integration of tools for the incremental refinement of queries and a retrieval mechanism that finds information associatively related to a query. Empirical evaluation of CodeFinder has demonstrated the effectiveness of the approach.     

Automatic content based image retrieval using semantic analysis

We present a new text-to-image re-ranking approach for improving the relevancy rate in searches. In particular, we focus on the fundamental semantic gap that exists between the low-level visual features of the image and high-level textual queries by dynamically maintaining a connected hierarchy in the form of a concept database. For each textual query, we take the results from popular search engines as an initial retrieval, followed by a semantic analysis to map the textual query to higher level concepts. In order to do this, we design a two-layer scoring system which can identify the relationship between the query and the concepts automatically. We then calculate the image feature vectors and compare them with the classifier for each related concept. An image is relevant only when it is related to the query both semantically and content-wise. The second feature of this work is that we loosen the requirement for query accuracy from the user, which makes it possible to perform well on users’ queries containing less relevant information. Thirdly, the concept database can be dynamically maintained to satisfy the variations in user queries, which eliminates the need for human labor in building a sophisticated initial concept database. We designed our experiment using complex queries (based on five scenarios) to demonstrate how our retrieval results are a significant improvement over those obtained from current state-of-the-art image search engines.     

Efficient Monte Carlo clustering in subspaces

One of the key difficulties for users in information retrieval is to formulate appropriate queries to submit to the search engine. In this paper, we propose an approach to enrich the user’s queries by additional context. We used the Language Model to build the query context, which is composed of the most similar queries to the query to expand and their top-ranked documents. Then, we applied a query expansion approach based on the query context and the Latent Semantic Analyses method. Using a web test collection, we tested our approach on short and long queries. We varied the number of recommended queries and the number of expansion terms to specify the appropriate parameters for the proposed approach. Experimental results show that the proposed approach improves the effectiveness of the information retrieval system by 19.23 % for short queries and 52.94 % for long queries according to the retrieval results using the original users’ queries.     

Assisting web search using query suggestion based on word similarity measure and query modification patterns

One of the useful tools offered by existing web search engines is query suggestion (QS), which assists users in formulating keyword queries by suggesting keywords that are unfamiliar to users, offering alternative queries that deviate from the original ones, and even correcting spelling errors. The design goal of QS is to enrich the web search experience of users and avoid the frustrating process of choosing controlled keywords to specify their special information needs, which releases their burden on creating web queries. Unfortunately, the algorithms or design methodologies of the QS module developed by Google, the most popular web search engine these days, is not made publicly available, which means that they cannot be duplicated by software developers to build the tool for specifically-design software systems for enterprise search, desktop search, or vertical search, to name a few. Keyword suggested by Yahoo! and Bing, another two well-known web search engines, however, are mostly popular currently-searched words, which might not meet the specific information needs of the users. These problems can be solved by WebQS, our proposed web QS approach, which provides the same mechanism offered by Google, Yahoo!, and Bing to support users in formulating keyword queries that improve the precision and recall of search results. WebQS relies on frequency of occurrence, keyword similarity measures, and modification patterns of queries in user query logs, which capture information on millions of searches conducted by millions of users, to suggest useful queries/query keywords during the user query construction process and achieve the design goal of QS. Experimental results show that WebQS performs as well as Yahoo! and Bing in terms of effectiveness and efficiency and is comparable to Google in terms of query suggestion time.     

Distribution-Driven Visualization of Volume Data

Feature detection and display are the essential goals of the visualization process. Most visualization software achieves these goals by mapping properties of sampled intensity values and their derivatives to color and opacity. In this work, we propose to explicitly study the local frequency distribution of intensity values in broader neighborhoods centered around each voxel. We have found frequency distributions to contain meaningful and quantitative information that is relevant for many kinds of feature queries. Our approach allows users to enter predicate-based hypotheses about relational patterns in local distributions and render visualizations that show how neighborhoods match the predicates. Distributions are a familiar concept to nonexpert users, and we have built a simple graphical user interface for forming and testing queries interactively. The query framework readily applies to arbitrary spatial data sets and supports queries on time variant and multifield data. Users can directly query for classes of features previously inaccessible in general feature detection tools. Using several well-known data sets, we show new quantitative features that enhance our understanding of familiar visualization results.     

基于吸收态随机行走的两阶段效用性查询推荐方法

搜索引擎已经成为人们获取信息的重要途径,然而对于用户而言如何构造一个合适的查询仍然是一项困难的工作.为了减轻用户搜索信息的负担,查询推荐技术应运而生并且已经成为当今搜索引擎不可或缺的组成部分.传统的查询推荐方法主要关注向用户推荐相关性查询,即推荐与源查询具有相近搜索意图的其他查询.然而查询推荐的根本目标是帮助用户成功完成其搜索任务,而不仅仅是找到相关性查询,尽管相关性查询有时也能得到有用的搜索结果.为了更好地满足用户的搜索目标,一种更直接的查询推荐方式是向用户推荐高效用性查询,即能够更好满足用户信息需求的查询.提出了一个基于吸收态随机行走的2阶段效用性查询推荐方法,该方法能够同时对用户的查询重构行为和查询点击行为进行建模并推导出查询的效用.在真实查询日志上的实验结果表明:新方法在评价指标查询相关率(query relevant ratio, QRR)和平均相关文档数(mean relevant document, MRD)上要显著优于其他5种基准方法.     

XML 关键词检索的查询理解

与纯文本文档集相比,使用语义标签标注的半结构化的XML文档集,有助于信息检索系统更好地理解待检索文档.同样,结构化查询,比如SQL,XQuery和Xpath,相对于纯关键词查询更加清晰地表达了用户的查询意图.这二者都能够帮助信息检索系统获得更好的检索精度.但关键词查询因其简单和易用性,仍被广泛使用.提出了XNodeRelation算法,以自动推断关键词查询的结构化信息(条件/目标节点类型).与已有的推断算法相比,综合了XML文档集的模式和统计信息以及查询关键词出现的上下文及其关联关系等推断用户的查询意图.大量的实验验证了该算法的有效性.     

The Gesture Disagreement Problem in Free-hand Gesture Interaction

Accurately understanding a user’s intention is often essential to the success of any interactive system. An information retrieval system, for example, should address the vocabulary problem (Furnas et al., 1987) to accommodate different query terms users may choose. A system that supports natural user interaction (e.g., full-body game and immersive virtual reality) must recognize gestures that are chosen by users for an action. This article reports an experimental study on the gesture choice for tasks in three application domains. We found that the chance for users to produce the same gesture for a given task is below 0.355 on average, and offering a set of gesture candidates can improve the agreement score. We discuss the characteristics of those tasks that exhibit the gesture disagreement problem and those tasks that do not. Based on our findings, we propose some design guidelines for free-hand gesture-based interfaces.     

一种基于领域的历史网页检索算法*

在历史网页检索系统中,存在着按时间顺序来对检索结果进行排序的特殊需求,在客观上要求系统能够比较准确地判断文档与查询词是否相关。针对这一特殊需求,引入领域的概念,将领域用于用户检索的表示,在领域的基础上设计了一种带衰减因子的BM25检索相关性计算算法。实验结果显示该检索算法是有效的,引入领域后检索结果的F值平均提高了56.68%。     

Continual queries for Internet scale event-driven informationdelivery

We introduce the concept of continual queries, describe the design of a distributed event-driven continual query system-OpenCQ, and outline the initial implementation of OpenCQ on top of the distributed interoperable information mediation system DIOM. Continual queries are standing queries that monitor update of interest and return results whenever the update reaches specified thresholds. In OpenCQ, users may specify to the system the information they would like to monitor (such as the events or the update thresholds they are interested in). Whenever the information of interest becomes available, the system immediately delivers it to the relevant users; otherwise, the system continually monitors the arrival of the desired information and pushes it to the relevant users as it meets the specified update thresholds. In contrast to conventional pull-based data management systems such as DBMSs and Web search engines, OpenCQ exhibits two important features: it provides push-enabled, event-driven, content-sensitive information delivery capabilities; and it combines pull and push services in a unified framework. By event-driven we mean that the update events of interest to be monitored are specified by users or applications. By content-sensitive, we mean the evaluation of the trigger condition happens only when a potentially interesting change occurs. By push-enabled, we mean the active delivery of query results or triggering of actions without user intervention     

Design and evaluation of a multi-recommendation system for local code search

Searching for relevant code in the local code base is a common activity during software maintenance. However, previous research indicates that 88% of manually composed search queries retrieve no relevant results. One reason that many searches fail is existing search tools’ dependence on string matching algorithms, which cannot find semantically related code. To solve this problem by helping developers compose better queries, researchers have proposed numerous query recommendation techniques, relying on a variety of dictionaries and algorithms. However, few of these techniques are empirically evaluated by usage data from real-world developers. To fill this gap, we designed a multi-recommendation system that relies on the cooperation between several query recommendation techniques. We implemented and deployed this recommendation system within the Sando code search tool and conducted a longitudinal field study. Our study shows that over 34% of all queries were adopted from recommendation; and recommended queries retrieved results 11% more often than manual queries.     

Data-Sharing P2P Networks with Semantic Approximation Capabilities

The synergy between peer-to-peer systems and semantic Web technologies supports large-scale sharing of semantically rich data, usually represented through schemas such as RDF. Peers rarely share the same vocabulary, so the resulting heterogeneity of data representations introduces new challenges for the efficient and effective retrieval of relevant information. The authors leverage the presence of semantic approximations between peers' schemas to improve query routing by identifying the peers that best satisfy the user's requests, and to inform users of the relevance of the returned answers through a ranking mechanism that promotes the most semantically related results.     

Metaform: updatable form screens and their application to the use of office metaphors in query language instruction

Abstract

A database interface language and system, called Metaform, which automatically generates multi-relational form screen interfaces for use by non-computer professionals has been developed. A form screen is a subset of the relational database, with a particular relation or combination of relations being represented. Through form screens, users can simultaneously query and update several relations in the database without having to know about its underlying structure. An overview of the Metaform system is presented and several examples of the use of the Metaform query language and update operators are described.

A series of ‘usability’ studies were conducted on a prototype of the Metaform system to examine the claims that the form concept aids computer-naive users in building complex database queries. These studies adopted the form screen concept to present six office paper work analogies to users to help them to understand the database retrieval concepts. The analogies of a file cabinet, a file folder, a stack of forms, a single form, a table of information on a form and a field of information were used in a two-staged training module.

At the end of each training sequence, users answered questions with the prototype and with paper and pencil which tapped their understanding of the database retrievals they were learning to perform. The results from these questionnaires were mixed. Users performed successful relational queries for simple retrievals and for those using existential quantifiers. They had difficulty with queries involving multiple steps and intermediate stages. Although users understood and used the analogies, they ran into difficulties with the ambiguities in the English statements of the queries, thus suggesting a need for another level of metaphors and/or problem representation tools not associated with the machine but with the user's comprehension of database retrieval problems.     

Metaform: updatable form screens and their application to the use of office metaphors in query language instruction

A database interface language and system, called Metaform, which automatically generates multi-relational form screen interfaces for use by non-computer professionals has been developed. A form screen is a subset of the relational database, with a particular relation or combination of relations being represented. Through form screens, users can simultaneously query and update several relations in the database without having to know about its underlying structure. An overview of the Metaform system is presented and several examples of the use of the Metaform query language and update operators are described.

A series of 'usability' studies were conducted on a prototype of the Metaform system to examine the claims that the form concept aids computer-naive users in building complex database queries. These studies adopted the form screen concept to present six office paper work analogies to users to help them to understand the database retrieval concepts. The analogies of a file cabinet, a file folder, a stack of forms, a single form, a table of information on a form and a field of information were used in a two-staged training module.

At the end of each training sequence, users answered questions with the prototype and with paper and pencil which tapped their understanding of the database retrievals they were learning to perform. The results from these questionnaires were mixed. Users performed successful relational queries for simple retrievals and for those using existential quantifiers. They had difficulty with queries involving multiple steps and intermediate stages. Although users understood and used the analogies, they ran into difficulties with the ambiguities in the English statements of the queries, thus suggesting a need for another level of metaphors and/or problem representation tools not associated with the machine but with the user's comprehension of database retrieval problems.     

Fuzzy User Modeling for Information Retrieval on the World Wide Web

Information retrieval from the World Wide Web through the use of search engines is known to be unable to capture effectively the information needs of users. The approach taken in this paper is to add intelligence to information retrieval from the World Wide Web, by the modeling of users to improve the interaction between the user and information retrieval systems. In other words, to improve the performance of the user in retrieving information from the information source. To effect such an improvement, it is necessary that any retrieval system should somehow make inferences concerning the information the user might want. The system then can aid the user, for instance by giving suggestions or by adapting any query based on predictions furnished by the model. So, by a combination of user modeling and fuzzy logic a prototype system has been developed (the Fuzzy Modeling Query Assistant (FMQA)) which modifies a user's query based on a fuzzy user model. The FMQA was tested via a user study which clearly indicated that, for the limited domain chosen, the modified queries are better than those that are left unmodified. Received 10 November 1998 / Revised 14 June 2000 / Accepted in revised form 25 September 2000     

On research issues regarding uncertain query processing in an office document retrieval system

TEXPROS (TEXT PROcessing System) is an intelligent document processing, system; it supports storing, extracting, classifying, categorizing, retrieving, and browsing information from a variety of office documents [76]. This article presents a retrieval subsystem for TEXPROS, which is capable of processing incomplete, imprecise, and vague queries, and providing semantically meaningful responses to the user. The design of the retrieval subsystem is highly integrated with various mechanisms for achieving these goals. First, a system catalog including a thesaurus is used to store the knowledge about the database. Second, there is a query transformation mechanism composed of context construction and algebraic query formulation modules. Given an incomplete or imprecise query, the context construction module searches the system for the required terms and constructs a query that has a complete and precise representation: The resulting query is then formulated into an algebraic expression. Third, in practice, the user may not have a clear idea of what he is searching for. A browing mechanism is employed for such situations to assist the user in the retrieval process. With the browser, vague queries can be entered into the system until sufficient information, is obtained to the extent that the user is able to construct a query for his request. Finally, when processing of queries fails by responding with a null answer to the user, a generalizer mechanism is used to give the user cooperative explanation for the null answer. The presented techniques will contribute to our research toward development of highly intelligent data processing facilities beyond the present scope of database technology.This work was supported, in part, by the New Jersey Institute of Technology under grant No. 421280 and by a grant from the AT&T Foundation.     

Fuzzy query translation for relational database systems

The paper presents a new method for fuzzy query translation based on the alpha-cuts operations of fuzzy numbers. This proposed method allows the retrieval conditions of SQL queries to be described by fuzzy terms represented by fuzzy numbers. It emphasizes friendliness and flexibility for inexperienced users. The authors have implemented a fuzzy query translator to translate user's fuzzy queries into precise queries for relational database systems. Because the proposed method allows the user to construct his fuzzy queries intuitively and to choose different retrieval threshold values for fuzzy query translation, the existing relational database systems will be more friendly and more flexible to the users.     

Evaluating refined queries in top-k retrieval systems

In many applications, users specify target values for certain attributes/features without requiring exact matches to these values in return. Instead, the result is typically a ranked list of "top k" objects that best match the specified feature values. User subjectivity is an important aspect of such queries, i.e., which objects are relevant to the user and which are not depends on the perception of the user. Due to the subjective nature of top-k queries, the answers returned by the system to an user query often do not satisfy the users need right away, either because the weights and the distance functions associated with the features do not accurately capture the users perception or because the specified target values do not fully capture her information need or both. In such cases, the user would like to refine the query and resubmit it in order to get back a better set of answers. While there has been a lot of research on query refinement models, there is no work that we are aware of on supporting refinement of top-k queries efficiently in a database system. Done naively, each "refined" query can be treated as a "starting" query and evaluated from scratch. We explore alternative approaches that significantly improve the cost of evaluating refined queries by exploiting the observation that the refined queries are not modified drastically from one iteration to another. Our experiments over a real-life multimedia data set show that the proposed techniques save more than 80 percent of the execution cost of refined queries over the naive approach and is more than an order of magnitude faster than a simple sequential scan.     

Query Expansion for Effective Retrieval Results of Hindi–English Cross-Lingual IR

Information retrieval (IR) is the science of identifying documents or sub-documents from a collection of information or database. The collection of information does not necessarily be available in only one language as information does not depend on languages. Monolingual IR is the process of retrieving information in query language whereas cross-lingual information retrieval (CLIR) is the process of retrieving information in a language that differs from query language. In current scenario, there is a strong demand of CLIR system because it allows the user to expand the international scope of searching a relevant document. As compared to monolingual IR, one of the biggest problems of CLIR is poor retrieval performance that occurs due to query mismatching, multiple representations of query terms and untranslated query terms. Query expansion (QE) is the process or technique of adding related terms to the original query for query reformulation. Purpose of QE is to improve the performance and quality of retrieved information in CLIR system. In this paper, QE has been explored for a Hindi–English CLIR in which Hindi queries are used to search English documents. We used Okapi BM25 for documents ranking, and then by using term selection value, translated queries have been expanded. All experiments have been performed using FIRE 2012 dataset. Our result shows that the relevancy of Hindi–English CLIR can be improved by adding the lowest frequency term.