Usage-based chunking of software architecture information to assist information finding

One of the key problems with Software Architecture Documents (ADs) 2 is the difficulty of finding in- formation required from them. Most existing studies focus on the production of ADs or Architectural Knowledge (AK) 3 , to allow them to support information finding. However, there has been little focus placed on the consumption of ADs. To address this, we postulate the existence of a concept of “usage- based chunks”of architectural information discoverable from consumers’ usage of ADs when they engage in information-seeking tasks. In a set of user studies, we have found evidence that such usage-based chunks exist and that useful chunks can be identified from one type of usage data, namely, consumer’s ratings of sections of ADs. This has implications for tool design to support the effective reuse of AK.

Towards developing an integrated model of information behaviour

This paper presents the results from a study of information behaviors in the context of people's everyday lives undertaken in order to develop an integrated model of information behavior (IB). 34 participants from across 6 countries maintained a daily information journal or diary – mainly through a secure web log – for two weeks, to an aggregate of 468 participant days over five months. The text-rich diary data was analyzed using a multi-method qualitative-quantitative analysis in the following order: Grounded Theory analysis with manual coding, automated concept analysis using thesaurus-based visualization, and finally a statistical analysis of the coding data. The findings indicate that people engage in several information behaviors simultaneously throughout their everyday lives (including home and work life) and that sense-making is entangled in all aspects of them. Participants engaged in many of the information behaviors in a parallel, distributed, and concurrent fashion: many information behaviors for one information problem, one information behavior across many information problems, and many information behaviors concurrently across many information problems. Findings indicate also that information avoidance – both active and passive avoidance – is a common phenomenon and that information organizing behaviors or the lack thereof caused the most problems for participants. An integrated model of information behaviors is presented based on the findings.

Towards an integrated model of information behavior

Information behavior models generally focus on one of many aspects of information behavior, either information finding, conceptualized as information seeking, information foraging or information sense-making, information organizing and information using. This ongoing study is developing an integrated model of information behavior. The research design involves a 2-week-long daily information journal self-maintained by the participants, combined with two interviews, one before, and one after the journal-keeping period. The data from the study will be analyzed using grounded theory to identify when the participants engage in the various behaviors that have already been observed, identified, and defined in previous models, in order to generate useful sequential data and an integrated model.

Understanding users’ information behaviors through information journals

Information behavior studies in the field of Library and Information Science (LIS) generally focus on one of many aspects of information behavior: information finding, information organizing, and information using. Information seeking is further specialized into information searching, information seeking, information foraging or information sense making. Spink and Cole (2006) highlighted the lack of integration across these various approaches and models of information behavior within LIS. Often, each approach provides a different language for similar processes (Spink & Cole, 2004), and it is sometimes hard for practicing information professionals to parse the various theories and models to see how they shape and affect the provision of information resources, services, and products. An integrated model of information behaviors that explains the key dimensions of how peoples’ contextual and situational dimensions affect their information needs and behavior will help information providers and LIS researchers alike with a framework that can help “depict and explain a sequence of behaviors by referring to relevant variables, rather than merely indicating a sequence of events… while indicating something about information needs and sources” (Case, 2002). This presentation presents an integrated model of peoples’ information behaviors based on research that studied participants’ information behaviors through a detailed daily information journal maintained for two weeks.

Mediaş Jewish Community Photograph Collection 1900-1930s bulk: undated

The collection contains more than 60 black and white photographs from the first decades of the 20th century found in the synagogue of Mediaş (Mediasch, Medgyes), Romania. The photographs were found in the process of an on-going clean-up and restoration project and for the most part are unidentified. The photographs are of community members and their relatives and friends; they consist of group family portraits, individual portraits, babies, and children. Some of the photographs originate from Mediaş and other nearby Transylvanian towns, while others were printed by foreign printing shops and were presumably sent to relatives living in Mediaş.

Precursors and prices : structuring the Quebec synthetic drug market

Information recueillie sur les marchés des drogues de synthèse est beaucoup moins avancée que les études sur d'autres marchés de drogues illicites. La classification relativement récente des drogues de synthèse comme substances illicites, couplée avec ses caractéristiques distinctes qui empêchent son observation, a entravé le développement d’évaluations complètes et fiables des caractéristiques structurelles des marchés. Le but de cet article est de fournir un aperçu fiable sur la dynamique interne du marché des drogues synthétiques, en particulier sur ses caractéristiques structurelles et organisationnelles. En utilisant l'information obtenue à partir de 365 drogues de synthèse saisies par les policiers pendant un an, cette étude sera la fusion de deux techniques, soit la composition des drogues illicites et des analyses économiques, afin de tirer des évaluations fiables des caractéristiques structurelles du marché du Québec de drogues synthétiques. Les résultats concernant l'analyse de la composition des drogues indiquent que le marché des drogues synthétiques au Québec est probablement composé d'un nombre élevé de petites structures, ce qui indique un marché compétitif. L'analyse économique a également fourni des informations complémentaires sur le marché des drogues. Selon la région géographique les couts de la production et les relations entre trafiquant et consommateur influencent le prix des drogues. Les résultats de cette recherche mettent l'accent sur la nécessité de concevoir des politiques qui tient compte des différences régionales dans la production de drogue et reflète la nature compétitive de ce marché.

Finding additional semantic entity information for search engines

Entity-oriented search has become an essential component of modern search engines. It focuses on retrieving a list of entities or information about the specific entities instead of documents. In this paper, we study the problem of finding entity related information, referred to as attribute-value pairs, that play a significant role in searching target entities. We propose a novel decomposition framework combining reduced relations and the discriminative model, Conditional Random Field (CRF), for automatically finding entity-related attribute-value pairs from free text documents. This decomposition framework allows us to locate potential text fragments and identify the hidden semantics, in the form of attribute-value pairs for user queries. Empirical analysis shows that the decomposition framework outperforms pattern-based approaches due to its capability of effective integration of syntactic and semantic features.

Diagnose this if you can: On the effectiveness of search engines in finding medical self-diagnosis information

An increasing amount of people seek health advice on the web using search engines; this poses challenging problems for current search technologies. In this paper we report an initial study of the effectiveness of current search engines in retrieving relevant information for diagnostic medical circumlocutory queries, i.e., queries that are issued by people seeking information about their health condition using a description of the symptoms they observes (e.g. hives all over body) rather than the medical term (e.g. urticaria). This type of queries frequently happens when people are unfamiliar with a domain or language and they are common among health information seekers attempting to self-diagnose or self-treat themselves. Our analysis reveals that current search engines are not equipped to effectively satisfy such information needs; this can have potential harmful outcomes on people’s health. Our results advocate for more research in developing information retrieval methods to support such complex information needs.

Finding within-organisation spatial information on the web

Information available on company websites can help people navigate to the offices of groups and individuals within the company. Automatically retrieving this within-organisation spatial information is a challenging AI problem This paper introduces a novel unsupervised pattern-based method to extract within-organisation spatial information by taking advantage of HTML structure patterns, together with a novel Conditional Random Fields (CRF) based method to identify different categories of within-organisation spatial information. The results show that the proposed method can achieve a high performance in terms of F-Score, indicating that this purely syntactic method based on web search and an analysis of HTML structure is well-suited for retrieving within-organisation spatial information.

Finding a Probe for Extracting Information on the Momentum Dependent-Interaction in Heavy Ion Collisions

The effect of momentum-dependent interaction on the kinetic energy spectrum of the neutron-proton ratio. <(n/p)(gas)>(b)(E-k) for Zn-64 + Zn-64 is studied. It is found that. <(n/p)(gas)>(b)(E-k) sensitively depends on the momentum-dependent interaction and weakly on the in- medium nucleon- nucleon cross section and symmetry potential. Therefore <(n/p)(gas)>(b)(E-k) is a possible probe for extracting information on the momentum-dependent interaction in heavy ion collisions.

Finding and Evaluation Information

A first year level introduction to finding and evaluating information (mostly on-line)

Look it up : finding information.

Para que los niños entre cinco y siete años aprendan acerca de cómo pueden encontrar la información que necesitan para realizar una tarea: como encontrar información en una biblioteca, qué tipo de información electrónica hay allí, buscar en el diccionario, etc. Este libro les ayuda a comparar las diferentes fuentes de información disponibles, mirar las diversas maneras en que se presenta la información, y pensar en por qué la necesitan. Tiene actividades, glosario y bibliografía.

Effectively finding relevant web pages from linkage information

This paper presents two hyperlink analysis-based algorithms to find relevant pages for a given Web page (URL). The first algorithm comes from the extended cocitation analysis of the Web pages. It is intuitive and easy to implement. The second one takes advantage of linear algebra theories to reveal deeper relationships among the Web pages and to identify relevant pages more precisely and effectively. The experimental results show the feasibility and effectiveness of the algorithms. These algorithms could be used for various Web applications, such as enhancing Web search. The ideas and techniques in this work would be helpful to other Web-related researches.

Finding the optimal cardinality value for information bottleneck method

Information Bottleneck method can be used as a dimensionality reduction approach by grouping “similar” features together [1]. In application, a natural question is how many “features groups” will be appropriate. The dependency on prior knowledge restricts the applications of many Information Bottleneck algorithms. In this paper we alleviate this dependency by formulating the parameter determination as a model selection problem, and solve it using the minimum message length principle. An efficient encoding scheme is designed to describe the information bottleneck solutions and the original data, then the minimum message length principle is incorporated to automatically determine the optimal cardinality value. Empirical results in the documentation clustering scenario indicates that the proposed method works well for the determination of the optimal parameter value for information bottleneck method.

Finding structures in information networks using the affinity network

This thesis proposes a novel graphical model for inference called the Affinity Network,which displays the closeness between pairs of variables and is an alternative to Bayesian Networks and Dependency Networks. The Affinity Network shares some similarities with Bayesian Networks and Dependency Networks but avoids their heuristic and stochastic graph construction algorithms by using a message passing scheme. A comparison with the above two instances of graphical models is given for sparse discrete and continuous medical data and data taken from the UCI machine learning repository. The experimental study reveals that the Affinity Network graphs tend to be more accurate on the basis of an exhaustive search with the small datasets. Moreover, the graph construction algorithm is faster than the other two methods with huge datasets. The Affinity Network is also applied to data produced by a synchronised system. A detailed analysis and numerical investigation into this dynamical system is provided and it is shown that the Affinity Network can be used to characterise its emergent behaviour even in the presence of noise.