Rule-based query personalization in digital libraries

Searching a digital library is typically a tedious task. A system can improve information access by building on knowledge about a user acquired in a user profile in order to customize information access both in terms of the information returned in response to a query (query personalization) as well as in terms of the presentation of the results (presentation personalization). In this paper, we focus on query personalization in digital libraries; in particular, we address structured queries involving metadata stored in relational databases. We describe the specification of user preferences at the level of a user profile and the process of query personalization with the use of query-rewriting rules.     

Predicting IR personalization performance using pre-retrieval query predictors

Code reviews consist in proof-reading proposed code changes in order to find their shortcomings such as bugs, insufficient test coverage or misused design patterns. Code reviews are conducted before merging submitted changes into the main development branch. The selection of suitable reviewers is crucial to obtain the high quality of reviews. In this article we present a new method of recommending reviewers for code changes. This method is based on profiles of individual programmers. For each developer we maintain his/her profile. It is the multiset of all file path segments from commits reviewed by him/her. It will get updated when he/she presents a new review. We employ a similarity function between such profiles and change proposals to be reviewed. The programmer whose profile matches the change most is recommended to become the reviewer. We performed an experimental comparison of our method against state-of-the-art techniques using four large open-source projects. We obtained improved results in terms of classification metrics (precision, recall and F-measure) and performance (we have lower time and space complexity).     

Helping online customers decide through Web personalization

The Web-based personalization system proposed here uses both collaborative filtering and Web usage mining to give online shoppers the personalized recommendations they need to purchase products more intelligently.     

E-service quality competition through personalization under consumer privacy concerns

One important factor that determines the quality of web-based customer service is the ability of a firm’s website to provide individual caring and attention. In this sense, online vendors try to offer varieties of web-based personalization. However, many previous studies show that there is an obvious trade off between personalization and customer privacy. The main objective of this research is, therefore, to verify the impact of consumers’ information privacy concerns on firms’ collection and use of consumer information for web-based personalization where firms compete with different levels of ability in consumer information utilization for personalization. Our result shows that a firm of inferior ability in customer information utilization is more affected by privacy concerns than a firm of superior ability in choosing to collect and use consumer information for personalization. However, this does not mean that a firm of inferior ability, which chooses not to provide personalization due to privacy concerns, is worse off than a firm of superior ability, which chooses to provide personalization, in generating profits. On the contrary, a firm of superior ability can become worse off than a firm with inferior ability.     

Eliciting user preferences through a guided design personalization process

In building industry, sound methods are needed to determine the design options that respond to the requirements of the potential house buyers. A new method is presented that consists of two main concepts: a user centred design approach through a non-designers interface and Bayesian networks for learning the housing preferences and responding to design choices. In a design session, the network is used to collect data about the housing preferences and to check for design inconsistencies. An experiment with the system in a housing project shows how the subjects responded to the system feedback on their design decisions.     

Engaging end-user driven recommender systems: personalization through web augmentation

Multimedia Tools and Applications - In the past decades recommender systems have become a powerful tool to improve personalization on the Web. Yet, many popular websites lack such functionality,...     

Object query optimization through detecting independent subqueries

The problem of query optimization in object-oriented databases is addressed. We follow the Stack-Based Approach to query languages, which employs the naming-scoping-binding paradigm of programming languages rather than traditional database concepts such as relational/object algebras or calculi. The classical environment stack is a semantic basis for definitions of object query operators, such as selection, projection/navigation, dependent join, and quantifiers. We describe a general object data model and define a formalized OQL-like query language SBQL. Optimization by rewriting concerns queries containing so-called independent subqueries. It consists in detecting them and then factoring outside loops implied by query operators. The idea is based on the formal static analysis of scoping rules and binding names occurring in a query. It is more general than the classical pushing selections/projections before joins.     

Supporting mobile database access through query by icons

In this paper, we present both the theoretical framework and a prototype of a query processing facility that supports the exploration and query of databases from a mobile computer through the manipulation of icons. Icons are particularly suitable for mobile computing since they can be manipulated without typing. The facility requires no special knowledge of the location or the content of the remote database nor understanding of the details of the database schema. Its iconic query language involves no path specification in composing a query. The query facility provides metaquery tools that assist in the formulation of complete queries in an incremental manner on the mobile computer and without involving access to the actual data in the remote database. By not requiring constant access and caching of the actual data, it is able to effectively cope with the inherent limitations in memory and battery life on the mobile computer, disconnections and restricted communication bandwidth, and the high monetary cost of wireless communication.Recommended by: Daniel Barbara, Ravi Jain and Narayanan Krishnakumar     

Structural analysis of polynomial-time query learnability

In this paper we study the computational power of polynomialtime query learning systems for several query types, and the computational complexity of a learning problem for several representation classes. As corollaries of our results, we prove some polynomial-time nonlearnability results, and relate polynomial-time learnability of some representation classes to the complexity of representation finding problems of P/poly oracles. For example, forCIR, a representation class by logical circuits, it is shown that P(NP ₁ ⁽⁾ ) is an upper bound of power of query learning systems forCIR, and that P(NP ₁ ⁽⁾ ) is also a lower bounds of power of query learning systems forCIR when they are used to learn a certain subclassR ofCIR. It is also shown that the problem of learningCIR is P(NP(NP ₁ ⁽⁾ ))-solvable. Then, using these results, the following relations are proved: (1) If, for someA P/poly, the representation finding problem ofA is not in P(NP ₁ ^A ), thenCIR is not polynomial-time query learnable even by using queries such as membership, equivalence, subset, superset, etc. (2) On the other hand, if the above-mentioned subclassR ofCIR is not polynomial-time query learnable by using subset and superset queries, then some B P/poly exists such that its representation finding problem is not in P(NP ₁ ^B ).This is an extended version of a part of the paper A Formal Study of Learning via Queries, which was presented at the 17th International Colloquium of Automata, Languages, and Programming. The first author was supported in part by Grant in Aid for Scientific Research of the Ministry of Education, Science, and Culture of Japan under Grant-in-Aid for Co-operative Research (A) 02302047 (1991). The second author was supported in part by ESPRIT II Basic Research Actions Program of the EC under Contract No. 3075 (project ALCOM), and by the National Science Foundation under Grant CCR89-13584, while visiting the University of California, Santa Barbara.     

Stratified analysis of AOL query log

Characterizing user’s intent and behaviour while using a retrieval information tool (e.g. a search engine) is a key question on web research, as it hold the keys to know how the users interact, what they are expecting and how we can provide them information in the most beneficial way. Previous research has focused on identifying the average characteristics of user interactions. This paper proposes a stratified method for analyzing query logs that groups queries and sessions according to their hit frequency and analyzes the characteristics of each group in order to find how representative the average values are. Findings show that behaviours typically associated with the average user do not fit in most of the aforementioned groups.     

高校财务多维查询分析系统

为了能够从多个角度查询财务数据,以支持分析和决策,提出了基于MDX(多维表达式)技术的财务分析器,实现从源数据库建立多维数据集,并利用MDX技术从多个维度查询财务数据,以及对结果进行数据分析的功能.在此基础上实现了结果导出Excel、用户和组管理、可访问数据库设置、表项别称设定等模块,以帮助管理、使用和支持决策.并通过实例测试与传统SQL查询进行比较,结果表明该分析器在效率上更加优秀且更适合于帮助分析决策.     

Enhancing electronic medical record retrieval through semantic query expansion

There are currently many active movements towards computerizing patient healthcare information. As Electronic Medical Record (EMR) systems are being increasingly adopted in healthcare facilities, however, there is a big challenge in effectively utilizing this massive information source. It is very time-consuming for healthcare providers to dig into the voluminous medical records of a patient to find the few that are indeed relevant to the patient’s current problem. Due to the complex semantic relationships among medical concepts and use of many synonyms, antonyms, and hypernym/hyponym, simple word-based information retrieval does not produce satisfactory results. In this paper, we propose an EMR retrieval system that leverages semantic query expansion to retrieve medical records that are relevant to the patient’s current symptom/problem. The proposed framework integrates various technologies, including information retrieval, domain ontologies, automatic semantic relationship learning, as well as a body of domain knowledge elicited from healthcare experts. Knowledge of semantic relationships among medical concepts, such as symptoms, exams and tests, diagnoses, and treatments, as well as knowledge of synonyms and hypernym/hyponyms, is used to expand and enhance initial queries posed by a user. We have implemented a preliminary prototype and conducted a pilot testing using sample nursing notes drawn from the EMR system of a community health center.     

Simple algorithm page layout analysis

An algorithm for page layout analysis (segmentation) is suggested in the paper. It allows whitespace between text blocks to be detected on a document page. The algorithm could be used in document analysis and recognition problems. In particular, it can be used for column recognition in multicolumn text and tables. The suggested algorithm is quite simple for implementation.     

Search for the Blind : A schema independent query method using concept hierarchy and keywords

Some database schema is very complex and it changes more frequently than the past. On the other hands, in such application database as scientific database and decision-support database, the importance of ad-hoc queries is expected to increase. Therefore the ability of a system to support ad-hoc queries without knowing the database schema information is needed. In this paper, we will describe a method to create candidate structured queries with concept hierarchy using user-input keywords. We will create structured queries in the following step; first, we extract the schema items related to keywords, and second, we compose possible paths between schema items, thus formulate candidate SQL queries.     

The persuasive effects of personalization through: name mentioning in a smoking cessation message

Mentioning the recipient’s name in a persuasive message is one way to personalize messages in an attempt to increase persuasion. However, this type of personalization may lead to a self-threat that activates defensive reactions and to a subsequent decrease in persuasion. A self-affirmation procedure that induces open-mindedness may prevent this drawback. Smokers were exposed to a message advocating smoking cessation in one of three experimental conditions presenting: A standard text, a text with the recipient’s name incorporated four times, or a text with the recipient’s name incorporated twelve times. The extent to which smokers indicated at pretest to value their health was used as a measure of personal relevance of the message, and tested as a moderator. Half of the smokers was exposed to a self-affirmation procedure before they read the text. The dependent variable was the intention to quit smoking. When health value was moderate, mentioning the recipient’s name twelve times induced a defensive reaction, significantly lowering persuasion. This was supported by the observation that self-affirmation prevented this effect. When health value was high, mentioning the recipient’s name twelve times significantly increased persuasion. However, when self-affirmation was applied, persuasion was significantly lowered. The effects of mentioning the recipient’s name depend on individual differences in personal relevance of the message (health value) before exposure to the message: Name mentioning can increase, but also decrease persuasion, through defensive processes.     

基于MapReduce的SQL查询优化分析

对SQL翻译成MapReduce程序的性能进行分析,并对影响翻译性能的原因进行阐述。结合MapReduce作业间输入相关性、数据转换相关性和作业流相关性的分析,通过合并冗余的作业,减少资源消耗,从而达到提高SQL查询性能的目的,给出了优化条件和优化规则。通过对优化前后的性能进行对比,证明改进后的SQL过程有更高的执行效率。     

Global Optimization through Rotation Space Search

This paper introduces a new algorithmic technique for solving certain problems in geometric computer vision. The main novelty of the method is a branch-and-bound search over rotation space, which is used in this paper to determine camera orientation. By searching over all possible rotations, problems can be reduced to known fixed-rotation problems for which optimal solutions have been previously given. In particular, a method is developed for the estimation of the essential matrix, giving the first guaranteed optimal algorithm for estimating the relative pose using a cost function based on reprojection errors. Recently convex optimization techniques have been shown to provide optimal solutions to many of the common problems in structure from motion. However, they do not apply to problems involving rotations. The search method described in this paper allows such problems to be solved optimally. Apart from the essential matrix, the algorithm is applied to the camera pose problem, providing an optimal algorithm. The approach has been implemented and tested on a number of both synthetically generated and real data sets with good performance. NICTA is funded by the Australian Government’s Backing Australia’s Ability initiative, in part through the Australian Research Council.     

面向航天器测试领域的可定制数据查询分析方法

基于当前航天器测试领域数据查询分析需求及应用系统的现状,提出了一种基于服务组合技术的可定制数据查询分析方法.给出了由数据查询分析基本服务生成组合服务的算法,解决了多变数据查询分析需求的动态满足的问题.实验表明,该方法可以动态生成数据查询处理逻辑,并正确可行.     

Reducing query overhead through route learning in unstructured peer-to-peer network

In unstructured peer-to-peer networks, such as Gnutella, peers propagate query messages towards the resource holders by flooding them through the network. This is, however, a costly operation since it consumes node and link resources excessively and often unnecessarily. There is no reason, for example, for a peer to receive a query message if the peer has no matching resource or is not on the path to a peer holding a matching resource. In this paper, we present a solution to this problem, which we call Route Learning, aiming to reduce query traffic in unstructured peer-to-peer networks. In Route Learning, peers try to identify the most likely neighbors through which replies can be obtained to submitted queries. In this way, a query is forwarded only to a subset of the neighbors of a peer, or it is dropped if no neighbor, likely to reply, is found. The scheme also has mechanisms to cope with variations in user submitted queries, like changes in the keywords. The scheme can also evaluate the route for a query for which it is not trained. We show through simulation results that when compared to a pure flooding based querying approach, our scheme reduces bandwidth overhead significantly without sacrificing user satisfaction.     

Context and page analysis for improved Web search

NECI Research Institute has developed a metasearch engine that improves the efficiency of Web searches by downloading and analyzing each document and then displaying results that show the query terms in concert. The paper discusses the features of the NECI metasearch engine