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This study describes a research methodology that evolved as a result of a longitudinal participative research effort at Ericsson. The study lasted six years and is built upon ten different, but interrelated research projects. The methodology developed was labeled table-tennis research to capture the intense interactions between researchers and organizational members throughout the life cycle of the study. This chapter reviews and briefly examines alternative interactive research approaches for knowledge creation and knowledge utilization in the context of the knowledge-based firm. The table-tennis research approach is advanced as an alternative inquiry approach for the generation of actionable knowledge. Through an in-depth inductive study, three clusters crucial to the development of actionable knowledge emerged: (1) the contextual dimensions, (2) the quality of the effort's focus, and (3) the quality of the table-tennis process itself. Within these clusters, variables and processes were inductively derived and linked together to form the process-model. Implications for the design of the knowledge-based firm, continuous innovation, change, learning and actionable knowledge creation are discussed.

1) The Alchemy of Intelligent IT (iIT) (Ning Zhong, Jiming Liu) Part I Emerging Data Mining Technology ======================================= 2) Grid-Based Data Mining and Knowledge Discovery (Mario Cannataro, Antonio Congiusta, Carlo Mastroianni, Andrea Pugliese, Domenico Talia, Paolo Trunfio) 3) The MiningMart Approach to Knowledge Discovery in Databases (Katharina Morik, Martin Scholz) 4) Ensemble Methods and Rule Generation (Yongdai Kim, Jinseog Kim, Jongwoo Jeon) 5) Evaluation Scheme for Exception Rule/Group Discovery (Einoshin Suzuki) 6) Data Mining for Direct Marketing (Ning Zhong, Yiyu Yao, Chunnian Liu, Chuangxin Ou, Jiajin Huang) Part II Data Mining for Web Intelligence ========================================= 7) Mining for Information Discovery on the Web (Hwanjo Yu, An Hai Doan, Jiawei Han) 8) Mining Web Logs for Actionable Knowledge (Qiang Yang, Charles X. Ling, Jianfeng Gao) 9) Discovery of Web Robot Sessions Based on Their Navigational Patterns (Pang-Ning Tan, Vipin Kumar) 10) Web Ontology Learning and Engineering (Roberto Navigli, Paola Velardi, Michele Missikoff) 11) Browsing Semi-Structured Texts on the Web Using Formal Concept Analysis (Richard Cole, Peter Eklund, Florence Amardeilh) 12) Graph Discovery and Visualization from Textual Data (Vincent Dubois, Mohamed Quafafou) Part III Emerging Agent Technology =================================== 13) Agent Networks: Topological and Clustering Characterization (Xiaolong Jin, Jiming Liu) 14) Finding the Best Agents for Cooperation (Francesco Buccafurri, Domenico Rosaci, Giuseppe L.M. Sarne, Luigi Palopoli) 15) Constructing Hybrid Intelligent Systems for Data Mining from Agent Perspectives (Zili Zhang, Zhengqi Zhang) 16) Making Agents Acceptable to People (Jeffrey M. Bradshaw, Patrick Beautement, Maggie R. Breedy, Larry Bunch, Sergey V. Drakunov, Paul J. Feltovich, Robert R. Hoffman, Renia Jeffers, Matthew Johnson, Shriniwas Kulkarnt, James Lott, Anil K. Raj, Niranjan Suri, Andrzej Uszok) Part IV Emerging Soft Computing Technology =========================================== 17) Constraint-Based Neural Network Learning for Time Series Predictions (Benjamin W. Wah, Minglun Qian) 18) Approximate Reasoning in Distributed Environments (Andrzej Skowron) 19) Soft Computing Pattern Recognition, Data Mining, and Web Intelligence (Sankar K. Pal, Sushmita Mitra, Pabitra Mitra) 20) Dominance-Based Rough Set Approach to Knowledge Discovery (I) (Salvatore Greco, Benedetto Matarazzo, Roman Slowinski) 21) Dominance-Based Rough Set Approach to Knowledge Discovery (II) (Salvatore Greco, Benedetto Matarazzo, Roman Slowinski) Part V Statistical Learning Theory =================================== 22) Mining Dependence Structures (I) -- A General Statistical Learning Perspective -- (Lei Xu) 23) Mining Dependence Strucutres (II) -- An Independence Analysis Perspective -- (Lei Xu)

Automated all source data fusion primarily fuses analyst generated messages. The Automated Targeting Data Fusion (ATDF) effort created an application that rapidly associated very diverse information sources automatically. In recent military conflict situations, enormous amounts of multi-source data have been made available to war-fighters. These data include imagery, signals intelligence, acoustic information, input from human analysts, and other sources. The significant magnitude of collected intelligence overwhelms analysts, operators, collection managers and commanders. Complicating matters further is that the data is collected, transmitted, processed, exploited and disseminated via multiple stovepipe architectures involving different types of intelligence and support personnel. It is difficult for diverse users to maintain situational awareness over the battle-space. This is an extremely crucial issue with rapid targeting decisions. This ATDF capability offers an alternative to automatically support each individual user as well as decision makers who must use all of their collective command-level decisions. ATDF rapidly associates diverse intelligence products as they are collected and fuses it into actionable knowledge in near real-time.

The Advanced Agent Technology Laboratory in the School of Computer Science at Carnegie Mellon University, the Human Computer Interaction Group at the University of Pittsburgh, the Munitions Directorate of Air Force Research Laboratory (MN/AFRL), Rome Labs and Northrup Grumman are partnering in early-stage research to address issues in high-level information fusion, including intent inferencing and retasking of sensors and munitions. To help translate information superiority to decision superiority (i.e. to rapidly arrive at better decisions than adversaries can respond to), many scientific, technical and technological challenges must be addressed. The most critical of those are information fusion and management at different levels, communication, planning and execution monitoring. The RETSINA multiagent infrastructure allows information producers and users to discover one another and establish direct links. The robust, decentralized Infosphere which results, can be stood up rapidly and ensures that information of the specified types will be delivered to the right users under the right conditions. We will present the overall approach for this research program, the role and contribution of each of the partners, the envisioned testbed, examples and research results to-date. In addition, we will discuss plans for the out-years.

This chapter presents two methods that combine data mining and decision support techniques with the aim to generate actionable knowledge. Both methods follow the same methodology in which data mining is used to support decision-making. The methodology consists of the following phases: business understanding; data acquisition, data understanding and preprocessing; data mining through subgroup discovery; subgroup evaluation; and deployment for decision support. The two methods have been applied to support decisionmaking in marketing.

Reducing Emissions from Deforestation and Degradation (REDD+) is a policy instrument meant to mitigate climate change while also achieving poverty reduction in tropical countries. It has garnered critics for homogenising environmental and development governance and for ignoring how similar efforts have tended to exacerbate gender inequalities. Nonetheless, regarding such schemes as inevitable, some feminists argue for requirements that include women′s empowerment and participation. In this paper we move beyond discussions about safeguards and examine whether the very framing of REDD+ programs can provide openings for a transformation as argued for by its proponents. Following the REDD+ policy process in Burkina Faso, we come to two important insights: REDD+ is a solution in need of a problem. Assumptions about gender are at the heart of creating ′actionable knowledge′ that enabled REDD+ to be presented as a policy solution to the problems of deforestation, poverty and gender inequality. Second, despite its ′safeguards′, REDD+ appears to be perpetuating gendered divisions of labour, as formal environmental decision-making moves upwards; and responsibility and the burden of actual environmental labour shifts further down in particularly gendered ways. We explore how this is enabled by the development of policies whose stated aims are to tackle inequalities.

In response to attacks against enterprise networks, administrators increasingly deploy intrusion detection systems. These systems monitor hosts, networks, and other resources for signs of security violations. The use of intrusion detection has given rise to another difficult problem, namely the handling of a generally large number of alarms. In this paper, we mine historical alarms to learn how future alarms can be handled more efficiently. First, we investigate episode rules with respect to their suitability in this approach. We report the difficulties encountered and the unexpected insights gained. In addition, we introduce a new conceptual clustering technique, and use it in extensive experiments with real-world data to show that intrusion detection alarms can be handled efficiently by using previously mined knowledge.

This paper discusses actionable knowledge generation. Actionable knowledge is explicit symbolic knowledge, typically presented in the form of rules, that allows the decision maker to recognize some important relations and to perform an action, such as targeting a direct marketing campaign, or planning a population screening campaign aimed at detecting individuals with high disease risk. The disadvantages of using standard classification rule learning for this task are discussed, and three subgroup discovery implementations are outlined. Case studies, a medical and two marketing ones, are used to present the lessons learned in solving problems requiring actionable knowledge generation.

This paper discusses actionable knowledge generation. Actionable knowledge is explicit symbolic knowledge, typically presented in the form of rules, that allows the decision maker to recognize some important relations and to perform an action, such as targeting a direct marketing campaign, or planning a population screening campaign aimed at targeting individuals with high disease risk. The disadvantages of using standard classification rule learning for this task are discussed, and a subgroup discovery approach proposed. This approach uses a novel definition of rule quality which is extensively discussed.

The people who brought us the bad news about climate change are making an effort to help us figure out what to do about it. At a meeting last month, subtitled "Climate Research in Service to Society," the new buzzword was "actionable": actionable science, actionable information, actionable knowledge. What9s needed is not simply data but processed information that an engineer sizing a storm-water pipe to serve for the next 50 years or a farmer in Uganda considering irrigating his fields can use to make better decisions in a warming world. Researchers preparing for the next international climate assessment, due in 2013, delivered some discouraging news for those striving to generate actionable science of use at the regional and local scale. But the challenges to the climate science community also include creating so-called climate services that can deliver actionable information effectively to users, speakers said.

Much of the discourse surrounding conventional evaluation is based on faith in the powers of ‘instrumental rationality’ amongst policy-makers (Parsons 2004). However, there is growing scepticism ov...

This Wuppertal Paper Environmental Protection in Learning Organizationshighlights the crucial methodological and theoretical assumptions which influencedand formed the design of the ADAPT-project about SMEs - Sustainability and theneeds of employees. Potentials for employment, qualification and participation of environmental management systems in small and medium-sized companies. ADAPT is a Community Initiative financed by funds of the European Social Fund (ESF) and by the Land North Rhine-Westphalia (NRW). ADAPT helps companiesand employees of the European Union to adapt structural changes in the industrial environment. The project is an European transfer project which analyses andcompares business experience in implementing environmental protection measureswith the premises of EMAS/ISO 14000. The primary aim of the project is toimprove participation and qualification in environmental management.The paper discusses the state of research and the future development necessities fora social science based design of environmental management systems and the improvement of sociological consulting practices. The project aims especially on the development and testing of new tools for environmental management systems whichenable corporate actors and practitioners much better to understand and to usetechniques of self-evaluation and continuous self-improvement implemented by theintervening researchers.After some initial thesis about future capabilities of enterprises, about challenges ofsocial science based environmental research and the relation of researchers andpractitioners in chapter 1, chapter 2 gives a brief overall view of the projects´ phasesand the methodology used. Chapter 3 focusses more detailed on the methodologicalbackground of the projekt design, particularly on action research and qualitativesocial science research and chapter 4 focusses on the theoretical assumptions, especially on the importance of micropolitic approaches and the analysis ofcollective action in the works of Michel Crozier and Erhard Friedberg and the organizational learning approach in the works of Chris Argyris and Donald A.Schön. The final chapter 5 draws - in relation to contemporary approaches andresearch on corporate environmental protection, micropolitics and organizationallearning - some first conclusions from the experiences made during the project´sproceedings.

The article mentions the aims and objectives of the "Academy of Management Learning & Education" and introduces four essays in this issue. Milton R. Blood focuses on the role of business schools in generating actionable knowledge. Peter Navarro asserts that macroeconomics is necessary in MBA programs. Scott Julian and Joseph C. Ofori-Dankwa comment on business school accreditation and competition status. Michael Harmon offers an argument that competition status is negatively affecting research, teaching, and social objectives.

This commentary notes that the authors of the Special Issue propose that organization design knowledge will be more actionable if it is created in collaboration with the organization’s members. I draw attention to a risk in the co-creation of design knowledge: increasing the politicization of the already politically fraught process of organizational design. The reasons why collaborative design-knowledge creation may increase politicking are described, and ideas for managing it are suggested.

Traditionally, knowledge actionability has been investigated mainly by developing and improving technical interestingness. Recently, initial work on technical subjective interestingness and business-oriented profit mining presents general potential, while it is a long-term mission to bridge the gap between technical significance and business expectation. In this paper, we propose a two-way significance framework for measuring knowledge actionability, which highlights both technical interestingness and domain-specific expectations. We further develop a fuzzy interestingness aggregation mechanism to generate a ranked final pattern set balancing technical and business interests. Real-life data mining applications show the proposed knowledge actionability framework can complement technical interestingness while satisfy real user needs. © 2007, Inderscience Publishers.

Most data mining algorithms and tools stop at discovered customer models, producing distribution information on customer profiles. Such techniques, when applied to industrial problems such as customer relationship management (CRM), are useful in pointing out customers who are likely attritors and customers who are loyal, but they require human experts to postprocess the mined information manually. Most of the postprocessing techniques have been limited to producing visualization results and interestingness ranking, but they do not directly suggest actions that would lead to an increase the objective function such as profit. Here, we present a novel algorithm that suggest actions to change customers from an undesired status (such as attritors) to a desired one (such as loyal) while maximizing objective function: the expected net profit. We develop these algorithms under resource constraints that are abound in reality. The contribution of the work is in taking the output from an existing mature technique (decision trees, for example), and producing novel, actionable knowledge through automatic postprocessing.

This era of groundbreaking scientific developments in high-resolution, high-throughput technologies is allowing the cost-effective collection and analysis of huge, disparate datasets on individual health. Proper data mining and translation of the vast datasets into clinically actionable knowledge will require the application of clinical bioinformatics. These developments have triggered multiple national initiatives in precision medicine—a data-driven approach centering on the individual. However, clinical implementation of precision medicine poses numerous challenges. Foremost, precision medicine needs to be contrasted with the powerful and widely used practice of evidence-based medicine, which is informed by meta-analyses or group-centered studies from which mean recommendations are derived. This “one size fits all” approach can provide inadequate solutions for outliers. Such outliers, which are far from an oddity as all of us fall into this category for some traits, can be better managed using precision medicine. Here, we argue that it is necessary and possible to bridge between precision medicine and evidence-based medicine. This will require worldwide and responsible data sharing, as well as regularly updated training programs. We also discuss the challenges and opportunities for achieving clinical utility in precision medicine. We project that, through collection, analyses and sharing of standardized medically relevant data globally, evidence-based precision medicine will shift progressively from therapy to prevention, thus leading eventually to improved, clinician-to-patient communication, citizen-centered healthcare and sustained well-being.

We present analytic framework for evidence-based management, design, and engineering of collaborative intranet environments. The analytics target elucidation of essential elements of human-system interactions. Temporal segmentation of human behavior in digital environments permits identification of crucial navigational points as well as higher order abstractions. Explorations of these elements provide fertile grounds for assessment of usability and behavioral characteristics that directly translate to actionable knowledge indispensable for improvements of collaboration portals. We extrapolate the analytic findings from a case study of a large scale collaborative organizational intranet; in order to identify three crucial domains facilitating alignment between observed evidence and best management and engineering practices.