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Process mining tools can help discover and improve the business processes of urban community services from historical service event records. However, for the community service domains with small datasets, the effects of process mining are generally limited due to process incompleteness and data noise. In this paper, a cross-domain knowledge transfer method is proposed to help service process discovery with small datasets by making use of rich knowledge in similar domains with large datasets.

First, ontology modeling is used to reduce the effects of cross-domain semantic ambiguity on knowledge transfer. Second, association rules (of the activities in the service processes) are extracted with Bayesian network. Third, applicable association rules are retrieved using an applicability assignment function. Further, the retrieved association rules in domains with large datasets are mapped to those with a small dataset using a linear programming method, with a heuristic miner being adopted to generate the process model.

The proposed method is verified based on the empirical data of 10 service domains from Beidaihe, China. Results show that process discovery performance of all 10 domains were improved with the overall robustness score, precision, recall and F1 score increased by 13%, 13%, 17% and 15%, respectively. For the domains with only small datasets, the cross-domain knowledge transfer method outperforms popular state-of-the art methods.

The limitations of sample sizes are greatly reduced. This scheme can be followed to establish business process management systems of community services with reasonable performance and limited sample sizes.

Knowledge discovery in databases (KDD) is a tedious and repetitive process. A challenge for the effective use of KDD is understanding and confirming its results derived from the harmonized process. To exploit the advantages of agents’ application, this paper aims to propose a conceptual model based on a multi-agent system (MAS) to control each step of the KDD process.

This paper reports the empirical findings of a survey conducted among academic and industrial sectors in Tehran, Iran. In this survey, the participants answered a questionnaire about the main factors of designing a suitable model for the KDD process based on MAS. The factor analysis reveals important insights of previous models developed by various researchers.

This research uses the survey results to find six critical success factors, continuity in refinement and improvement; learning and acting concurrently; loosely or tightly coupled approach for using technologies; cooperative, dynamic and flexible environment; documentation and reporting; and extracting and evaluating knowledge intelligently, for a proper conceptual model of the KDD process based on MAS.

The proposed model reflects all aspects of the KDD process by applying the intelligent agents for each process steps. In addition, this research only considers the Iran society; hence, it cannot be generalized to other nations, and it may need further research in other countries and to be implemented in real-world business domains.

This research helps organizations to adopt a proposed model and implement a KDD process to advantage the valuable knowledge that exists in their data resources.

The algorithm of disjoint literature‐based knowledge discovery provides a convenient, efficient and effective auxiliary method for scientific research. Based on an analysis of Swanson's A‐B‐C model of disjoint literature‐based knowledge discovery and Gordon's intermediate literature theory, this paper seeks to propose a more comprehensive compound correlation model for disjoint literature‐based knowledge discovery.

A new algorithm of vector space model (VSM) based disjoint literature‐based knowledge discovery is designed to implement the compound correlation model.

The validity tests showed that this new model not only simulated both of Swanson's early and well‐known discoveries of Raynaud's disease‐fish oil and migraine‐magnesium connections successfully, but also applied to knowledge discovery in the agricultural economics literature in the Chinese language.

Although the workload was reduced to the minimum under the compound correlation model compared with other algorithms and models, part of the work needed some manual intervention in the process of disjoint literature‐based knowledge discovery with the VSM‐based compound correlation model.

The algorithm was capable of knowledge discovery with a large‐scale dataset and had an advantage in identifying a series of hidden connections among a set of literatures. Therefore, application of the model might be extended to more fields.

Traditional two‐step knowledge discovery procedures were integrated into the model, which contained open and closed disjoint literature‐based knowledge discovery.

The purpose of this paper is to focus on the distinction between smart specialisation and smart specialisation policy and it studies under what conditions a smart specialisation policy is necessary.

A conceptual framework is built based on historical evidence of successful dynamics of structural changes at regional level qualified as “smart specialisation”. The identification of market and coordination failures that are likely to impede the occurrence of spontaneous process of smart specialisation makes a good case for a smart specialisation policy.

The paper highlights important design principles for the policy process that should help to minimise potential risks of policy failures and policy capture.

The paper does assess the effect of smart specialisation on innovation and growth at regional level because it is too early to observe and measure effects. The paper confines itself to conjectures about the effects of such a policy.

The paper makes recommendations and explains some of the practicalities about the implementation of the policy at regional level.

The paper is one of the first dealing with the topic of smart specialisation policy.

Examines the relationship between systems and their users from the knowledge discovery perspective. Recently knowledge discovery in databases has made important progress, but it may also bring some potential problems to database design, such as issues related to database security, as an unauthorized user may derive highly sensitive knowledge from unclassified data. There is a need for a comprehensive study on knowledge discovery in human‐computer symbiosis. Borrowing terms from algorithm design and artificial intelligence literature, proposes a notion called database‐user adversarial partnership. This notion is general enough to cover various knowledge discovery and security issues related to databases and their users. Furthermore, the notion of database‐user adversarial partnership can be further generalized into system‐user adversarial partnership. Opportunities provided by knowledge discovery techniques and potential social implications are discussed and illustrated by examples.

Despite growth in use of inter-organisational relationships for knowledge co-creation, many collaborations struggle to realise the synergistic benefits of these networks. This paper aims to explore the evolving dialectic tensions evident within an inter-organisational relationship and the governance consideration to optimise the knowledge process.

A longitudinal case of a university-industry knowledge network is selected for study. The single case analysis aligns with the dialectical epistemology, which dismisses the expectation of homogeny or constancy across network cases.

The research highlights the circular condition between dialectic tensions evident within inter-organisational relations and the governance mechanisms developed to synthesis the network knowledge discovery capability. The research shows that these tensions are a natural part of the network existence and often advantageous to knowledge creation. The research also highlights that governance is required at multiple levels within the network entity to optimise knowledge exchange and discovery.

The research adds to the limited application of dialectical thinking to inter-organisational networks. It highlights the structural and relational governance mechanisms that interplay to optimise their knowledge process capability. The research also highlights the multiple levels within networks at which tensions can originate, requiring knowledge governance at the micro, meso and macro level to address the complexity of the inter-organisational relationship. This research provides a better understanding of how knowledge within inter-organisational relations can be managed for mutual benefit and value creation.

The aim of this work is to increase awareness of the potential of the technique of text mining to discover knowledge and further promote research collaboration between knowledge management and the information technology communities. Since its emergence, text mining has involved multidisciplinary studies, focused primarily on database technology, Web-based collaborative writing, text analysis, machine learning and knowledge discovery. However, owing to the large amount of research in this field, it is becoming increasingly difficult to identify existing studies and therefore suggest new topics.

This article offers a systematic review of 85 academic outputs (articles and books) focused on knowledge discovery derived from the text mining technique. The systematic review is conducted by applying “text mining at the term level, in which knowledge discovery takes place on a more focused collection of words and phrases that are extracted from and label each document” (Feldman et al., 1998, p. 1).

The results revealed that the keywords extracted to be associated with the main labels, id est, knowledge discovery and text mining, can be categorized in two periods: from 1998 to 2009, the term knowledge and text were always used. From 2010 to 2017 in addition to these terms, sentiment analysis, review manipulation, microblogging data and knowledgeable users were the other terms frequently used. Besides this, it is possible to notice the technical, engineering nature of each term present in the first decade. Whereas, a diverse range of fields such as business, marketing and finance emerged from 2010 to 2017 owing to a greater interest in the online environment.

This is a first comprehensive systematic review on knowledge discovery and text mining through the use of a text mining technique at term level, which offers to reduce redundant research and to avoid the possibility of missing relevant publications.

Starting from a critical analysis of the main criteria currently used to identify marginal areas, this paper aims to propose a new classification model of such territories by leveraging knowledge discovery approaches and knowledge visualization techniques, which represent a fundamental pillar in the knowledge-based urban development process.

The methodology adopted in this study relies on the design science research, which includes five steps: problem identification, objective definition, solution design and development, demonstration and evaluation.

Results demonstrate how to exploit knowledge discovery and visualization to obtain multiple mappings of inner areas, in the aim to identify good practices and optimize resources to set up more effective territorial development strategies and plans. The proposed approach overcomes the traditional way adopted to map inner areas that uses a single indicator (i.e. the distance between a municipality and the nearest pole where it is possible to access to education, health and transportation services) and leverages seven groups of indicators that represent the distinguishing features of territories (territorial capital, social costs, citizenship, geo-demography, economy, innovation and sustainable development).

The proposed model could be enriched by new variables, whose value can be collected by official sources and stakeholders engaged to provide both structured and unstructured data. Also, another enhancement could be the development of a cross-algorithms comparison that may reveal useful to suggest which algorithm can better suit the needs of policy makers or practitioners.

This study sets the ground for proposing a decision support tool that policy makers can use to classify in a new way the inner areas, thus overcoming the current approach and leveraging the distinguishing features of territories.

This study shows how the availability of distributed knowledge sources, the modern knowledge management techniques and the emerging digital technologies can provide new opportunities for the governance of a city or territory, thus revitalizing the domain of knowledge-based urban development.

As a critical component in the entrepreneurial process, knowledge is essential to the study of how entrepreneurs compete under constraints. Research in this area is challenged by the unobservable and imprecise nature of knowledge which inhibits advanced theory building and testing, and we explore this problem by analyzing the relationship between the entrepreneurial process, constraints to the process, and knowledge flows. We apply and extend a systems-theoretic framework that identifies the knowledge system in entrepreneurial organizations, and develop an integrative model to guide future research.

Knowledge discovery in databases aims to discover useful and significant information from multiple databases. However, in the remote sensing field, the large size of discovered information makes it hard to manually look for interesting information quickly and easily. The purpose of this paper is to automate the process of identifying interesting spatiotemporal knowledge (expressed as rules).

The proposed approach is based on case‐based reasoning (CBR) process. CBR allows the recognition of useful and interesting rules by simulating a human reasoning process, and combining objective and subjective interestingness measures. It takes advantage of statistics' power from objective criteria and the reliability of subjective criteria. This helps improve the discovery of interesting rules by taking into consideration the different properties of interestingness measures.

The proposed approach combines several interestingness measures with complementary properties to improve the detection of the interesting rules. Based on a CBR process, it, also, offers three main advantages to users in a remote sensing field: automatism, integration of the users' expectations and combination of several interestingness measures while taking into account the reliability of each one. The performance of the proposed approach is evaluated and compared to other approaches using several real‐world datasets.

This study reports a valuable decision support tool for engineers, environmental authority and personnel who want to identify relevant discovered rules. The resulting rules are useful for many fields such as: disaster prevention and monitoring, growth volume and crops on farm or grassland, planting status of agricultural products, and tree distribution of forests.