Automation vs. Human Intervention

Purpose The extraction of relevant knowledge from data is called knowledge discovery (KD). The KD process requires a large amount of data and it must be reliable before mining. Complexity is not only in deriving knowledge from data but also in improving system performance with a psycho-cognitive approach. KD demands a high level of human cognition and mental activity to generate and retrieve knowledge. Therefore, this study aims to explain how psychological knowledge is involved in KD. Design/methodology/approach By understanding the cognitive processes that lead to knowledge production, KD can be improved through interventions that target psychological processes, such as attention, learning and memory. In addition, psycho-cognitive approaches can help us to better grasp the process of KD and the factors that influence its effectiveness. The study attempted to correlate interdependence by interpreting cognitive approaches to KD from a psychological perspective. The authors of this paper draw on both primary and secondary literary warrants to empirically prove psychological bending in KD. Findings Understanding the psychological aspects of data and KD can identify the development of tools, process and environments that support individual and teams in making sense of data and extracting valuable knowledge. The study also finds that interdisciplinary collaboration, bringing together expertise in psychology, data science and domain specific knowledge fosters effective KD processes. Originality/value The KD system cannot function well and will not be able to achieve its full potential without psycho-cognitive foundation. It was found that KD in the KD system is influenced by human cognition. The authors made a contribution to KD by fusing psycho-cognitive approaches with data-driven technology and machine learning.

This chapter provides a research frame for geographic information scientists to study the integration of geospatial data mining and knowledge discovery. It examines the current state of Data mining (DM) and Knowledge discovery data (KDD) technology, identifies special needs for geospatial DM and KDD, and outlines research challenges and the significance to national research needs. The chapter explores research frontiers in geographic knowledge discovery briefly and propose a research agenda to highlight short-term, midterm, and long-term objectives. The University Consortium for Geographic Information Science seeks to facilitate a multidisciplinary research effort on the development of geospatial DM/KDD science and technology. Geographic data has unique properties that require special consideration and techniques. Geographic information exists within highly dimensioned geographic measurement frameworks. Knowledge-based Geographic Information Science attempts to build higher-level geographic knowledge into digital geographic databases for analyzing complex phenomena. The development of DM and knowledge discovery tools must be supported by a solid geographic foundation.

Recent advancements in Large Language Model (LLM) agents have demonstrated remarkable potential in automatic knowledge discovery. However, rigorously evaluating an AI's capacity for knowledge discovery remains a critical challenge. Existing benchmarks predominantly rely on static datasets, leading to inevitable data contamination where models have likely seen the evaluation knowledge during training. Furthermore, the rapid release cycles of modern LLMs render static benchmarks quickly outdated, failing to assess the ability to discover truly new knowledge. To address these limitations, we propose DBench-Bio, a dynamic and fully automated benchmark designed to evaluate AI's biological knowledge discovery ability. DBench-Bio employs a three-stage pipeline: (1) data acquisition of rigorous, authoritative paper abstracts; (2) QA extraction utilizing LLMs to synthesize scientific hypothesis questions and corresponding discovery answers; and (3) QA filter to ensure quality based on relevance, clarity, and centrality. We instantiate this pipeline to construct a monthly-updated benchmark covering 12 biomedical sub-domains. Extensive evaluations of SOTA models reveal current limitations in discovering new knowledge. Our work provides the first dynamic, automatic framework for assessing the new knowledge discovery capabilities of AI systems, establishing a living, evolving resource for AI research community to catalyze the development of knowledge discovery.

System for managing and refining structural characteristics discovered from databases

In recent years both the number and the size of organisational databases have increased rapidly. However, although available processing power has also grown, the increase in stored data has not necessarily led to a corresponding increase in useful information and knowledge. This has led to a growing interest in the development of tools capable of harnessing the increased processing power available to better utilise the potential of stored data. The terms Knowledge Discovery in Databases and Mining have been adopted for a field of research dealing with the automatic discovery of knowledge implicit within databases. Data mining is useful in situations where the volume of data is either too large or too complicated for manual processing or, to a lesser extent, where human experts are unavailable to provide knowledge. The success already attained by a wide range of data mining applications has continued to prompt further investigation into alternative data mining techniques and the extension of data mining to new domains. This paper surveys, from the standpoint of the database systems community, current issues in data mining research by examining the architectural and process models adopted by knowledge discovery systems, the different types of discovered knowledge, the way knowledge discovery systems operate on different data types, various techniques for knowledge discovery and the ways in which discovered knowledge is used.

The increasing use of computers in all the areas of human activities is resulting in huge collections of digital data. Databases are common everywhere and are used as repositories of every kind of data. Knowledge discovery techniques and tools are used today to analyze those very large data sets to identify interesting patterns and trends in them. When data is maintained over geographically distributed sites the computational power of distributed and parallel systems can be exploited for knowledge discovery in databases. In this scenario the Grid can provide an effective computational support for distributed knowledge discovery on large data sets. To this purpose we designed a system called Knowledge Grid This chapter describes the Knowledge Grid architecture and discusses some related systems and models recently proposed for knowledge discovery on Grids. The chapter presents also how to design and implement distributed data mining applications by using the Knowledge Grid tools starting from searching Grid resources, composing software and data elements, and executing the resulting application on a Grid.

Knowledge discovery is a compute and data intensive process that allows for finding patterns, trends, and models in large datasets. The Grid can be effectively exploited for deploying knowledge discovery applications because of the high-performance it can offer and its distributed infrastructure. For effective use of Grids in knowledge discovery, the development of middleware is critical to support data management, data transfer, data mining and knowledge representation. To such purpose, we designed the Knowledge Grid, a high-level environment providing for Grid-based knowledge discovery tools and services. Such services allow users to create and manage complex knowledge discovery applications, composed as workflows that integrate data sources and data mining tools provided as distributed Grid services. This chapter describes the Knowledge Grid architecture and describes how its components can be used to design and implement distributed knowledge discovery applications. Then, the chapter describes how the Knowledge Grid services can be made accessible using the Open Grid Services Architecture (OGSA) model.

Pedestrian navigation using the sense of touch

Here, we present a new approach based on manifold learning for inverse design and knowledge discovery in nanophotonics. We present the unique capabilities of manifold learning approaches for reducing the dimensionality of the high-dimensional relationships in photonic nanostructures. We show how this can help to understand the underlying patterns in the responses of such nanostructures. Such a visualization in the low-dimensional space enables knowledge discovery and studying the underlying physics of nanostructures and can facilitate the inverse design. We also use this method to study the role of the design parameters and design a class of nanostructure while reducing the design complexity.

Knowledge-discovery systems face challenging problems from real-world databases, which tend to be dynamic, incomplete, redundant, noisy, sparse, and very large. These problems are addressed and some techniques for handling them are described. A model of an idealized knowledge-discovery system is presented as a reference for studying and designing new systems. This model is used in the comparison of three systems: CoverStory, EXPLORA, and the Knowledge Discovery Workbench. The deficiencies of existing systems relative to the model reveal several open problems for future research. >

Five high-quality workshops were held at the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009) in Bangkok, Thailand during April 27-30, 2009. There were 17, 6, 9, 4 and 5 accepted papers to be presented at the Pacific Asia Workshop on Intelligence and Security Informatics (PAISI 2009), the workshop on Advances and Issues in Biomedical Data Mining (AIBDM 2009), the workshop on Data Mining with Imbalanced Classes and Error Cost (ICEC 2009), the workshop on Open Source in Data Mining (OSDM 2009), and the workshop on Quality Issues, Measures of Interestingness and Evaluation of Data Mining Models (QIMIE 2009). One competition, PAKDD 2009 Data Mining Competition, and one local workshop, Thai Track Session, were arranged. From these workshops (except PAISI which published its works in separate LNCS proceedings), we selected two or three best papers for this LNCS publication. PAKDD is a major international conference in the areas of data mining (DM) and knowledge discovery in database (KDD). It provides an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition and automatic scientific discovery,data visualization, causal induction and knowledge-based systems.

In the recent years, the evolution in multimedia technology has accelerated the growth of multimedia data. Even though the multimedia data are heterogeneous, the rich information they carry has made a high demand for sophisticated multimedia knowledge discovery systems. To mine the knowledge from multimedia document, each type of multimedia data has to undergo unique processing and knowledge discovery processes because of its uniqueness. However, this procedure of processing and analyzing each type of multimedia data separately may make the system more complicated in case of large databases. Alternatively, it will be more advantageous if heterogeneous objects are represented in a common domain, such that the similar processing and knowledge discovery methods can be used. Motivated by this concept, a method known as domain converter is proposed to represent the heterogeneous multimedia objects in a spatial domain. Also based on information theory, a similarity measure is proposed to find the similarity between the documents. To evaluate the proposed framework, the experiments have been conducted for the retrieval of multimedia documents. The proposed domain converter represents the multimedia document in a homogeneous domain, and with the proposed similarity measure, better document retrieval rate has been achieved.

Discovering and using knowledge from unsupervised data

Knowledge discovery in databases, or data mining, is an important objective in the development of data- and knowledge-base systems. An attribute-oriented rough set method is developed for knowledge discovery in databases. The method integrates learning from example techniques with rough set theory. An attribute-oriented concept tree ascension technique is first applied in generalization, which substantially reduces the computational complexity of the database learning processes. Then the rough set techniques are applied to the generalized relation to derive different knowledge rules. Moreover, the approach can find all the maximal generalized rules in the data. Based on these principles, a prototype database learning system, DBROUGH, has been constructed. Our study shows that attribute-oriented induction combined with rough set techniques provide an efficient and effective mechanism for knowledge discovery in database systems.

Knowledge discovery in databases is a process that aims at the discovery of associations within data sets. The analysis of geo-referenced data demands a particular approach in this process. This chapter presents a new approach to the process of knowledge discovery, in which qualitative geographic identifiers give the positional aspects of geographic data. Those identifiers are manipulated using qualitative reasoning principles, which allows for the inference of new spatial relations required for the data mining step of the knowledge discovery process. The efficacy and usefulness of the implemented system — Padrão — has been tested with a bank dataset. The results support that traditional knowledge discovery systems, developed for relational databases and not having semantic knowledge linked to spatial data, can be used in the process of knowledge discovery in geo-referenced databases, since some of this semantic knowledge and the principles of qualitative spatial reasoning are available as spatial domain knowledge.