Using Real-Life Troubleshooting Interactions to Inform Self-assistance Design

Research on keyword search in database community has achieved a lot of success, and areas of interests have been moved from keyword search in relational databases to various advanced issues such as keyword search in multimedia data and data streams. Yet, many fundamental issues on keyword search in traditional databases remain. One such issue is how to interpret users' information needs behind keywords they provided. A common approach of many prototype systems is to make such interpretation as a designer's choice (such as imposing AND or OR semantics, or a combination), leaving no choice to the users. A much more meaningful approach would be allowing users themselves to specify the required semantics through contextual information. So can we build a system which stays with the simplicity of Keyword search, yet can incorporate the contextual information provided in the user query? In this paper we introduce STRUCT to explore this idea. STRUCT takes English language queries involving intended keywords; we refer to such search as English query search. Instead of resorting on a full-fledged natural language processing, the unneeded words in the queries are discarded. Only the specific contextual information along with the keywords containing database contents will be used to construct SQL queries. The contextual information is used to interpret the meaning of the queries, including the semantics involving AND, OR and NOT. In this paper we describe the architecture of STRUCT, the procedure of English query processing (parsing), the basic idea of the grouping algorithm, SQL query construction and sample results of experiments.

The VIBE (Visual Information Browsing Environment) prototype system, which was developed at Molde College in Norway in conjunction with researchers at the University of Pittsburgh, allows users to evaluate documents from a retrieved set that is graphically represented as geometric icons within one screen display. While the formal modeling behind VIBE and other information visualization retrieval systems is well known, user interaction with the system is not. This investigation tested the designer assumption that VIBE is a tool for a smart (expert) user and asked: What are the effects of the different levels of user expertise upon VIBE usability? Three user groups including novices, online searching experts, and VIBE system experts totaling 31 participants were tested over two sessions with VIBE. Participants selected appropriate features to complete tasks, but did not always solve the tasks correctly. Task timings improved over repeated use with VIBE and the nontypical visually oriented tasks were resolved more successfully than others. Statistically significant differences were not found among all parameters examined between novices and online experts. The VIBE system experts provided the predicted baseline for this study and the VIBE designer assumption was shown to be correct. The study's results point toward further exploration of cognitive preattentive processing, which may help to understand better the novice/expert paradigm when testing a visualized interface design for information retrieval.

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.

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.

Keyword search in relational databases has recently emerged as a new research topic. As a search result is often assembled from multiple relational tables, existing IR-style ranking strategies can not be applied directly. In this paper, we propose a novel IR ranking strategy considering query semantics for effective keyword search. The experimental results on a large-scale real database demonstrate that our method results in significant improvement in terms of retrieval effectiveness as compared to previous ranking strategies.

Keyword-based search in relational database is an easy and effective way for ordinary users or Web users to access relational databases. Even though relational database management systems (RDBMs) have provided full-text search capabilities, they do not support keyword-based search model. The text databases and relational databases are different that is a challenging task to apply the keyword search techniques in information retrieval (IR) to DB. A common method to performing keyword search in relational database is to generate the minimum connected tuple sets in schema graph transformed from relations. Although existing candidate network (CN) generation methods retrieve a set of joining tuples, they are still problem which is causing large overhead for CNs generation. In this paper, we propose a new candidate network generation algorithm (Heuristic_CNGen) based on the iterative deepening A* (IDA*) algorithm. The proposed algorithm produces a minimum number of CNs according to the maximum number of tuple set. We generate CNs for a given keyword query. And then, we identify the connected tuple tree as a result according to generated CNs. We evaluate the proposed method on DBLP.

Set intersection is an important operation and widely used in both database and graph analytics applications. However, existing state-of-the-art set intersection methods only consider the size of input sets and fail to optimize for the case in which the intersection size is small. In real-world scenarios, the size of most intersections is usually orders of magnitude smaller than the size of the input sets, e.g., keyword search in databases and common neighbor search in graph analytics. In this paper, we present FESIA, a new set intersection approach on modern CPUs. The time complexity of our approach is O(n/√w + r), in which w is the SIMD width, and n and r are the size of input sets and intersection size, respectively. The key idea behind FESIA is that it first uses bitmaps to filter out unmatched elements from the input sets, and then selects suitable specialized kernels (i.e., small function blocks) at runtime to compute the final intersection on each pair of bitmap segments. In addition, all data structures in FESIA are designed to take advantage of SIMD instructions provided by vector ISAs with various SIMD widths, including SSE, AVX, and the latest AVX512. Our experiments on both real-world and synthetic datasets show that our intersection method achieves more than an order of magnitude better performance than conventional scalar implementations, and up to 4x better performance than state-of-the-art SIMD implementations.

With the prevalence of Internet access and online services, various big graphs are generated in many real applications (e.g., online social networks and knowledge graphs). An important task on analyzing and mining these graphs is keyword search. Essentially, given a graph G and query Q associated with a set of keywords, the keyword search aims to find a substructure (e.g., rooted tree or subgraph) S in G such that nodes in S collectively cover part of or all keywords in Q, and in the meanwhile, S is optimal on some user specified semantics. Keyword search on graphs can be applied in many real-life applications, such as point-of-interests recommendation and web search facility. In spite of the great importance of graph keyword search, we, however, notice that the latest survey on this topic is far out of date. Consequently, there is prompt need to conduct a comprehensive survey in this research direction. Motivated by this, in this survey, we systematically review graph keyword search studies by classifying the existing works into different categories according to the specific problem definition. This survey aims to provide the researchers a comprehensive understanding of existing graph keyword search solutions.

Customizable neuroinformatics database system: XooNIps and its application to the pupil platform

It has become desirable to provide a way of keyword search for users to query structured information in an XML database (data-centric retrieval) by combining database and information retrieval techniques. Therefore, the key challenges of keyword search in the XML database are how to define appropriate result models meeting user’s search intents, how to search the results by using efficient algorithms, and how to ranking the results. In this chapter, on one hand, the authors present the foundational knowledge of XML keyword search such as XML data models, XML query languages, inverted index, and Dewey encoding. On the other hand, some existing typical researches of keyword search in XML are presented, including the results models such as Smallest Lowest Common Ancestor (SLCA), Exclusive Lowest Common Ancestor (ELCA), Meaningful Lowest Common Ancestor (MLCA), the related search algorithms, and the ranking approaches.

The aim of this study is to analyse the development of publications of scientific articles focusing on the project-based learning model. The method used is a literature review with a bibliometric analysis approach. A keyword search in the Scopus journal database for “Project-Based Learning; Science Education” was used to start the research. An analysis was carried out using the Vosviewer application, which allows in-depth exploration of trends and key contributions in the field. The results showed that a total of 702 publications were recorded in Scopus-indexed journals over a fifteen-year period, from 2010 to 2024. The United States emerged as the country that contributed the most publications, followed by Indonesia in second place, reflecting its active role in promoting research related to the Project based learning model. Among researchers, Capraro, Robert M. was identified as the most prolific contributor during the period 2010-2024. In addition, the keyword ‘project-based learning’ appeared most frequently in the articles, confirming its centrality in discussions of innovative educational practices. These findings suggest a significant and growing interest in Project Based Learning in science education. However, this study highlights the need for a broader scope of research. Future research should not be limited to Scopus-indexed journals, but should also include various databases and resources.

Purpose: The purpose of this paper is to review and present that separate capabilities of knowledge management (KM) are identified and classified as sequential capability. These KM capabilities are utilized to affect organizational performance with a KM-cyclic view. Design/methodology/approach: This paper is a literature review research, through which KM capability and performance are identified, synthesized, from related books, literature and other research studies. Using key-word search in electronic databases, we generated a fairly exhaustive list of the articles dealing with the topic of KM capability in the period of 1979 to 2010 as well as various sources were extended from searching results. A total of 60 articles were identified for KM capability and 30 articles for performance measurement. Findings: The results of the research suggest two main aspects: 1) The KM capabilities were organized as four categories with cyclic view: acquisition/creation, conversion, sharing, and application. 2) The KM performance measurements were classified as two categories: non-financial and financial measurements. Research limitations/implications: There is a need to undertake empirical research and in-depth case studies of knowledge management practices using a KM cyclic view as a framework with which to interpret findings. Originality/value: The KM cyclic view is used to make some recommendations on appropriate research approaches to further our understanding of relationship of KM capability and performance in research. This also provides future researchers with useful means to assess the KM performance in different KM capability and to realize the benefits of a knowledge-based economy.

High-Variability Phonetic Training (HVPT) has demonstrated effectiveness in second language (L2) acquisition. This study utilizes a meta-analysis to explore the influence of high variability on learning L2 pronunciation and identifies the factors that moderate this impact. The studies were collected using a keyword search in the SCOPUS database. In total, our meta-analysis incorporated 18 primary studies that presented results obtained from experimental and control group designs, encompassing a total of 22 effect sizes. The results of our meta-analysis revealed that the overall effect size of HVPT on L2 pronunciation was medium (g = 0.77). Specifically, the effect size was large for consonant sounds and lexical tones, particularly pronounced when utilized by advanced learners. For beginners, the effect size was medium. The context in which the training was conducted also played a role, with a medium effect size observed when sentences were used. Additionally, the number of talkers involved in the study influenced the effect size, with a medium effect size found when 5 to 8 talkers were included. In other cases, the reported effect sizes were either small or non-significant.

This study aims to analyze the impact of artificial intelligence (AI) technology on organizational development (OD) and organizational learning (OL) from an educational perspective in the era of the Fourth Industrial Revolution. The study comprehensively reviews existing AI-related literature in the field of organizational development and organizational learning to analyze its effectiveness. A total of 36 relevant academic papers were selected through keyword searches in domestic and international academic databases using both Korean and English search terms related to AI, OD and OL. The selected papers were analyzed based on their research methods, research subjects, and key thematic characteristics. As a result of the analysis, a total of 6 upper-level concepts and 32 lower-level concepts were identified, including task automation and efficiency improvement, data-based decision-making, customized learning, problem-solving ability improvement, risk management, data ethics, and personal information protection. Based on these results, we intend to contribute to suggesting future research directions for organizational development and organizational learning linked to AI.