Spatial and temporal-based query disambiguation for improving web search

Queries submitted to search engines are ambiguous in nature due to users’ irrelevant input which poses real challenges to web search engines both towards understanding a query and giving results. A lot of irrelevant and ambiguous information creates disappointment among users. Thus, this research proposes an ambiguity evolvement process followed by an integrated use of spatial and temporal features to alleviate the search results imprecision. To enhance the effectiveness of web information retrieval the study develops an enhanced Adaptive Disambiguation Approach for web search queries to overcome the problems caused by ambiguous queries. A query classification method was used to filter search results to overcome the imprecision. An algorithm was utilized for finding the similarity of the search results based on spatial and temporal features. Users’ selection based on web results facilitated recording of implicit feedback which was then utilized for web search improvement. Performance evaluation was conducted on data sets GISQC_DS, AMBIENT and MORESQUE comprising of ambiguous queries to certify the effectiveness of the proposed approach in comparison to a well-known temporal evaluation and two-box search methods. The implemented prototype is focused on ambiguous queries to be classified by spatial or temporal features. Spatial queries focus on targeting the location information whereas temporal queries target time in years. In conclusion, the study used search results in the context of Spatial Information Retrieval (S-IR) along with temporal information. Experiments results show that the use of spatial and temporal features in combination can significantly improve the performance in terms of precision (92%), accuracy (93%), recall (95%), and f-measure (93%). Moreover, the use of implicit feedback has a significant impact on the search results which has been demonstrated through experimental evaluation.SHAHID KAMA

Concepts of relevance in a semiotic framework applied to ISAD (information systems analysis and design).

Relevance is the critical criterion for valuing information. The usual requirements of valuable information resources are their accuracy, brevity, timeliness and rarity. This thesis points out that relevance has to be explicitly recognised as an important quality of information. Therefore, the theory of signs is adopted to enable a systematic study of the problem of relevance according to the branches of semiotics in order to clarify the concept of information. Relevance has several meanings according to the various disciplinary approaches including phenomenology, law, logic, information science, communication and cognition. These different concepts are discussed and criticised in two chapters. A new approach is proposed in which a universal concept of relevance is considered as an affordance. Therefore, all the approaches to relevance can be applied within the broader approach of the analysis of affordances. This approach not only encompasses all the underlying characteristics of relevance, it is also compatible with the assumptions of the logic of norms and affordances (NORMA). NORMA semantic analysis is used as a basis on which concepts of relevance are applied semiotically. Two case- studies are selected for testing these concepts which results in a guideline for practical application in a semiotic framework. The results from these case-studies confirm the practical importance of these concepts of relevance which can be systematically used in the analysis and design of information systems. It also reaffirms the underlying characteristics of relevance which exist in the context of social reality

Information Behavior of People Diagnosed with a Chronic Serious Health Condition: A Longitudinal Study.

This study consisted of a longitudinal investigation into the information behavior of people diagnosed with a particular chronic serious health condition, type 2 diabetes. This study sought to identify the factors that motivate or impede the information seeking and use of these individuals and to discover how these factors and their influences change across time. It also aimed to uncover how they become aware of and capable of articulating their information needs, how they look for and make use of health-related information, and how these processes change across time. Lastly, it sought to discover what sources and types of diabetes-related information they perceive to be useful and how their perceptions of usefulness change as their knowledge about, and their experience with, diabetes transform across time. A longitudinal, mixed method approach was taken in which data were collected through two interview sessions spaced approximately four to six months apart. These sessions explored the experiences of 34 adults with type 2 diabetes, using a combination of qualitative and quantitative data collection techniques, including semi-structured interview, background questionnaires, health condition questionnaires, card-sorting exercises, and timeline elicitation. Both qualitative and quantitative techniques were used to analyze the data. The findings from this study provide evidence that information behavior plays a very important role in enabling participants to physically, cognitively, and affectively cope with having diabetes. Participants who rated diabetes-related information as more useful rated their general health higher and indicated that they felt less confused, more optimistic, and more in control of their experience with diabetes. This study’s findings also show that time forms a critical dimension within the context of consumer health information behavior. Participants’ information seeking and use practices, as well as their perceptions regarding the usefulness of diabetes-related information, also underwent important transformations across time. Moreover, their willingness and ability to act on this information also varied. Participants were not always immediately aware of their information needs and this state, termed “incognizance” here, sometimes led to serious health consequences. Having information at the point in time when it could be of the most use to them was of paramount importance.Ph.D.InformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91570/1/bstjean_1.pd

Context Aware Data Reduction for Highly Automated Driving

This research addresses the emerging challenge of data handling in the development of automated driving systems. The increasing volumes of data generated in the development and operation of these systems necessitate efficient handling strategies. A comprehensive review of the current state of the art reveals data reduction as a viable solution to manage these large data volumes. Further, an analysis of the state of the art establishes various challenges where established methodologies are insufficient, such as the open context. This leads to the primary research question of this dissertation: (1) how to effectively implement data reduction while addressing these challenges. The proposed solution in this work is a novel data reduction approach that integrates the concept of relevance, aiming to precisely define the informational needs within the data. This approach hypothesizes an enhanced control over performance loss in subsequent use cases, leading to two secondary research questions: (2) How can relevance be formally defined to facilitate its use in data reduction? (3) What is the impact of this data reduction method on the performance of subsequent use cases? To answer the second question, the concept of relevance is extensively explored in the literature. A general relevance model for automated driving is developed, along with a methodology for deriving and validating use case-specific relevance models. Application of this methodology to a selected use case demonstrates its effectiveness. Addressing the third question, an architecture for relevance-guided data reduction is proposed. A prototype implementing this architecture is evaluated in the contexts of perception and neural network training, focusing on semantic segmentation and object detection tasks. The findings indicate that relevance-guided data reduction can effectively control performance loss in perception tasks. However, in neural network training, a strong task dependency is observed, highlighting limitations of the approach and opportunities for future research. In conclusion, this work represents a contribution in two areas. First, to overcoming the challenges of handling large amounts of automotive data and reducing this data to only those parts with relevant information. Second, to an explicit consideration of the wide variety of relevance concepts in the development of automated driving