Testing user interaction with a prototype visualization‐based information retrieval system

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

Today, there are plenty of tools and techniques to perform text- or image-based classification of large datasets, targeting different levels of user expertise and abstraction. Specialists usually collaborate in projects by creating ground truth datasets and do not always have deep knowledge in Information Retrieval. This article presents a full platform for assisted binary classification of very large textual and text and image composed documents. Our goal is to enable human users to classify collections of several hundred thousand documents in an assisted way, within a humanly acceptable number of clicks. We propose a graphical user interface, based on several classification assistants: text- and image-based event detection, Active Learning (AL), search engine and rich visual metaphors to visualize the results. We also propose a novel query strategy in the context of Active Learning, considering the top unlabeled bi-grams and duplicated (e.g. re-tweeted) content in the target corpus to classify. These contributions are supported not only by a tool whose code is freely accessible but also by an evaluation of the impact of using the aforementioned methods on the number of clicks needed to reach a stable level of accuracy.

Technical troubleshooting is a domain that has changed enormously in recent years. Instead of relying on visits from service personnel end users facing technical problems with machinery, for example computers and printers, can now seek assistance from systems that guide them toward an autonomous solution of the problem. Systems that can be offered to them are wide in their range, but typically fall either in the category of Expert Systems or searchable databases that can be queried with keyword searches. Both approaches present advantages and disadvantages in terms of flexibility to address different levels of user expertise and ease of maintenance. However, few studies explicitly address the issue of how best to design for a balance between guidance and user freedom in such systems. In the work reported here an office equipment manufacturer’s call centre was studied in order to understand the mechanisms used when human agents guide users toward a resolution. The overall aim here is not to reproduce the agent behaviour in a system, but rather to identify which interactional building blocks such a system should have. These are assessed in relation to the existing online knowledge base resources offered by the same company in order to exemplify the kinds of issues designers need to attend to in this domain.

Eye-tracking data often provide access to information about users’ strategies and preferences that extend beyond purely behavioral data. Thanks to modern eye-tracking technology, gaze can be tracked rather unobtrusively in real-world settings. Here we examine the usefulness of gaze tracking with a mobile eye-tracker for interface design in an industrial setting, specifically the operation of a food production line. We use a mock task that is similar in its interface usage to the actual production task in routine machine operation. We compare two interface designs to each other as well as two levels of user expertise. We do not find any effects of experience or interface type in the behavioral data - in particular, both user groups needed the same time to complete the task on average. However, gaze data reveals different strategies: users with high experience in using the interface spend significantly less time looking at the screen – that is, actually interacting with the interface – in absolute terms as well as expressed as fraction of the total time needed to complete the task. This exemplifies how gaze tracking can be utilized to uncover different user-dependent strategies that would not be accessible through behavioral data alone.

This article explores the convergence of user interface design and cognitive psychology and digital sustainability, with the suggestion that expertise-specific UI concepts are a key solution to building more efficient and environmentally aware digital experiences. The article will illustrate how interfaces that are set to achieve a certain level of user expertise and integrate design paradigms like minimalism, adaptive interfaces, and emotion-responsive systems can bring about a reduction in cognitive load and the use of computational resources at the same time. The article compiles the results of various fields to form the links between cognitive effectiveness and environmental sustainability and points to interfaces being inherently sustainable in digital ecosystems due to less time spent on interaction, lower error rates, and better use of available resources. The article also examines wider implications on digital inclusion, compliance, and performance of an organization, as expertise-specific design is discussed as a multidimensional approach to not only human but also environmental goals in an increasingly digital world.

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.

PurposeThis paper aims to describe a study of the queries generated from a user experiment for cross‐language information retrieval (CLIR) from a historic image archive.Design/methodology/approachA controlled lab‐based user study was carried out using a prototype Italian‐English image retrieval system. Participants were asked to carry out searches for 16 images provided to them, a known‐item search task. Italian speaking users generated 618 queries for a set of known‐item search tasks. User's interactions with the system were recorded and queries were analysed manually quantitatively and qualitatively. The queries generated by user's interaction with the system were analysed and the results used to suggest recommendations for the future development of cross‐language retrieval systems for digital image libraries.FindingsResults highlight the diversity in requests for similar visual content and the weaknesses of machine translation for query translation. Through the manual translation of queries the authors show the benefits of using high‐quality translation resources. The results show the individual characteristics of users while performing known‐item searches and the overlap obtained between query terms and structured image captions, highlighting the use of user's search terms for objects within the foreground of an image.Research limitations/implicationsThis research looks in depth into one case of interaction and one image repository. Despite this limitation, the discussed results are likely to be valid across other languages and image repositories.Practical implicationsTo develop effective systems requires studying user's search behaviours, particularly in digital image libraries.Originality/valueThe growing quantity of digital visual material in digital libraries offers the potential to apply techniques from CLIR to provide cross‐language information access services. The value of this paper is in the provision of empirical evidence to support recommendations for effective cross‐language image retrieval system design.

Not many XML information retrieval (IR) systems exist that allow easy addition of and switching between different IR models. Especially for the scientific environment where building a system takes a lot of time and keeps researchers away from the real work, i.e., investigating what is the most effective IR model, a platform that would provide this functionality would be ideal. For this reason we developed such an XML IR system. It is centered around a logical algebra, named score region algebra (SRA), that enables transparent specification of IR models for XML databases (see [1] for more details). The transparency is achieved by a possibility to instantiate various retrieval models, using abstract score functions within algebra operators, while logical query plan and operator definitions remain unchanged. Our algebra operators model three important aspects of XML IR: element relevance score computation, element score propagation, and element score combination. To implement a new IR model, one only needs to provide definitions for these abstract function classes. To illustrate the usefulness of our algebra our demo system supports several, well known IR scoring models (e.g., Language Models, Okapi, and tf.idf), combined with different score propagation and combination functions. The user can select which model to use at run time. Following good practice in database systems design, our prototype system has a typical three-layered architecture. (1) The conceptual layer takes a NEXI [3] query expression as input, e.g.,

Observations of information retrieval (IR) system user experiences reveal a strong desire for collaborative search while at the same time suggesting that collaborative capabilities are rarely, and then only in a limited fashion, supported by current searching and visualization tools. Equally interesting is the fact that observations of user experiences with group support systems (GSS) reveal that although access to external information and the ability to search for relevant material is often vital to the progress of GSS sessions, integrated support for collaborative searching and visualization of results is lacking in GSS systems. After reviewing both user experiences described in IR and GSS literature and observing and interviewing users of existing IR and GSS commercial and prototype systems, the authors conclude that there is an obvious demand for systems supporting multi-user IR. It is surprising to the authors that very little attention has been given to the common ground shared by these two important research domains. With this in mind, our paper describes how user experiences with IR and GSS systems have shed light on a promising new area of collaborative research and led to the development of a prototype that merges the two paradigms into a collaborative information retrieval environment (CIRE). The paper presents a theory developed from initial user experiences with our prototype and describes plans to test the efficacy of this new paradigm empirically through controlled experimentation.

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.

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.

To properly handle concurrent accesses to documents by updates and queries in information retrieval (IR) systems, efforts are on to integrate IR features with database management system (DBMS) features. However, initial research has revealed that DBMS features optimized for traditional databases, display degraded performance while handling text databases. Since efficiency is critical in IR systems, infrastructural extensions are necessary for several DBMS features, transaction support being one of them. This paper focuses on developing efficient transaction support for IR systems where updates and queries arrive $dynamically$, by exploiting the data characteristics of the indexes as well as of the queries and updates that access the indexes. Results of performance tests on a prototype system demonstrate the superior performance of our algorithms.

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.

Multimodal image retrieval, which involves retrieving images using various modalities such as text, audio, or other images, has significant research importance due to its wide-ranging applications and potential to enhance user experiences across multiple domains. Traditional image retrieval systems, content-based image retrieval (CBIR) systems rely solely on visual features, which can be limiting. By integrating multiple modalities, multimodal retrieval systems can offer more robust and accurate results. A research problem is considered multimodal when it integrates information from more than one type of data source. In multimodal image retrieval (MMIR) systems, one form of data is used to search for outcomes in the same or different modalities. In contrast, cross-modal systems strictly retrieve information from a different modality. A significant challenge in these systems is the effective comparison of input-output queries from different data types, due to their basic forms and the subjective nature of content similarity. Researchers have proposed various techniques to address this challenge and to bridge the semantic gap in information retrieval across different modalities. This article presents comprehensive analysis of various research works pertained in this multimodal image retrieval. The results and comparative analysis of current research works on benchmark datasets have also been discussed. At the end of the paper, some of open issues are presented for future research directions.

The main problem in this thesis is the role of the librarian in improving the information retrieval system in the school library of SMA N 1 Bukit Sundi. This study aims to identify and explain the role of librarians in information retrieval systems, and what obstacles are faced by librarians and users in information retrieval systems.The research method used in this research is the method qualitative research using descriptive type. Collection technique The data used are observation, interviews, and documentation. Technique The data analysis used is data reduction, data presentation, data withdrawal conclusion and verification. Guarantee the validity of the data in this study using triangulation of techniques, sources and time. The results of the study concluded that the librarian plays an important role in the information retrieval system. The role of the librarian in the information retrieval system is generally in the processing section such as cataloging, classifying library materials, and determining subject headings to facilitate data entry and information retrieval in the retrieval system. In addition, in terms of processing library materials, book inventory activities, labeling, barcode attachment, and book bag attachment are also carried out. In the service section, the librarian's role is more about returning and borrowing library materials,provide guidance to users regarding procedures for information retrieval, use of libraries, use of independent services and librarians also serve users if someone comes directly to ask the librarian about the information needed quickly and accurately. Librarians also act as educators (educators) as educational librarians, providing education such as mini-meetings once a year to provide education to users on how to use catalogs, managers, administrators, and also supervisors in information retrieval systems.Keywords: Librarian, Retrieval System