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Formal Concept Analysis (FCA) is a natural framework to learn from examples. Indeed, learning from examples results in sets of frequent concepts whose extent contains mostly these examples. In terms of association rules, the above learning strategy can be seen as searching the premises of rules where the consequence is set. In its most classical setting, FCA considers attributes as a non-ordered set. When attributes of the context are partially ordered to form a taxonomy, Conceptual Scaling allows the taxonomy to be taken into account by producing a context completed with all attributes deduced from the taxonomy. The drawback, however, is that concept intents contain redundant information. In this article, we propose a parameterized algorithm, to learn rules in the presence of a taxonomy. It works on a non-completed context. The taxonomy is taken into account during the computation so as to remove all redundancies from intents. Simply changing one of its operations, this parameterized algorithm can compute various kinds of concept-based rules. We present instantiations of the parameterized algorithm to learn rules as well as to compute the set of frequent concepts.

We present an application of formal concept analysis aimed at representing a meaningful structure of knowledge communities in the form of a lattice-based taxonomy. The taxonomy groups together agents (community members) who develop a set of notions. If no constraints are imposed on how it is built, a knowledge community taxonomy may become extremely complex and difficult to analyze. We consider two approaches to building a concise representation, respecting the underlying structural relationships while hiding superfluous information: a pruning strategy based on the notion of concept stability and a representational improvement based on nested line diagrams and "zooming". We illustrate the methods on two examples: a community of embryologists and a community of researchers in complex systems.

One motivation of Grid computing is to aggregate the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal, efficient task scheduling algorithms are essential. However, scheduling algorithms in the Grid present high diversities that need to be classified. In this paper, with the help of an abstract scheduling architecture, some key features of the task scheduling problem in the Grid are discussed, followed by a taxonomy of the scheduling algorithms. Some typical examples are given in each category to present a picture of the current research and help to find new research problems.

Single sample face recognition (SSFR) is a challenging research problem in which only one face image per person is available for training. Moreover, the face image may have different pose, expression, illumination, occlusion etc. rendering this problem more complex. Several methods have been suggested by various researchers in literature to solve SSFR. Here, we provide a comprehensive review of the methods proposed in the last decade for solving SSFR problem and introduce a novel taxonomy for the same. We divide SSFR methods broadly into five categories viz. (i) feature based, (ii) virtual sample generation based, (iii) generic database based, (iv) Hybrid and (v) other methods. We have also briefly reviewed the face databases used for evaluating single sample face recognition methods. Furthermore, the performance of the methods has been analyzed in terms of classification accuracy as given in literature. At last, we also suggest some future direction to the researchers and practitioners working in this fascinating research area.

A definition of expert systems is given, its pragmatic demands are cited and its structure is described. The methods and ways of pattern recognition are used in the subsystems DIALOGUE, ANALYTIC and HOMEOSTAT. The recognition algorithms which work on the information to be retained in the data base and knowledge base are described. The problems of recognition appearing under the construction of expert systems are noted.

In the early 2000s, the Internet meant being able to connect different communication devices, whereas the focus in the last few years is on connecting “things” to the Internet. Although there is no distinct classification for these devices and things on the Internet, the Internet of Things (IoT) ecosystem primarily consists of a complex network of devices, sensors, and things. These “things” are controlled by humans and utilize the existing cloud infrastructure. These devices provide facilities and benefits to make our lives comfortable. IoT domains include smart homes, healthcare, manufacturing, smart wearables, smart cities, smart grids, industrial IoT, connected vehicles, and smart retail. Different IoT models involve human-to-IoT, IoT-to-IoT, IoT-to-traditional systems architectures. In most scenarios, the architecture ends up connecting to the unsecured Internet. This has thrown open several critical issues leading to cybersecurity attacks on IoT devices. IoT communications, protocols or the architecture were never been conceptualized to handle the new age cybersecurity attacks. IoT devices have limited compute, storage, network, or memory. In this research, the authors present a unique IoT attack framework named IAF focusing on the impact of IoT attacks on IoT applications and service levels. The authors also proposed an all-inclusive attack taxonomy classifying various attacks on IoT ecosystems.

Knowledge management (KM) in software engineering and software process improvement (SPI) are challenging. Most existing KM and SPI frameworks are too expensive to deploy or do not take an organization's specific needs or knowledge into consideration. There is thus a need for scalable improvement approaches that leverage knowledge already residing in the organizations.

This paper presents the Practice Selection Framework (PSF), an Experience Factory approach, enabling lightweight experience capture and use by utilizing postmortem reviews. Experiences gathered concern performance and applicability of practices used in the organization, gained from concluded projects. Project managers use these as decision support for selecting practices to use in future projects, enabling explicit knowledge transfer across projects and the development organization as a whole. Process managers use the experiences to determine if there is potential for improvement of practices used in the organization. This framework was developed and subsequently validated in industry to get feedback on usability and usefulness from practitioners. The validation consisted of tailoring and testing the framework using real data from the organization and comparing it to current practices used in the organization to ensure that the approach meets industry needs. The results from the validation are encouraging and the participants' assessment of PSF and particularly the tailoring developed was positive.

In Agile Software Development (ASD) effort estimation plays an important role during release and iteration planning. The state of the art and practice on effort estimation in ASD have been recently identified. However, this knowledge has not yet been organized. The aim of this study is twofold: (1) To organize the knowledge on effort estimation in ASD and (2) to use this organized knowledge to support practice and the future research on effort estimation in ASD. We applied a taxonomy design method to organize the identified knowledge as a taxonomy of effort estimation in ASD. The proposed taxonomy offers a faceted classification scheme to characterize estimation activities of agile projects. Our agile estimation taxonomy consists of four dimensions: estimation context, estimation technique, effort predictors and effort estimate. Each dimension in turn has several facets. We applied the taxonomy to characterize estimation activities of 10 agile projects identified from the literature to assess whether all important estimation-related aspects are reported. The results showed that studies do not report complete information related to estimation. The taxonomy was also used to characterize the estimation activities of four agile teams from three different software companies. The practitioners involved in the investigation found the taxonomy useful in characterizing and documenting the estimation sessions.

Designing cooperative multi-robot systems (MRS) requires expert knowledge both in control and artificial intelligence. Formation control is an important research within the research field of MRS. Since many researchers use different ways in approaching formation control, we try to give a taxonomy in order to help researchers design formation systems in a systematical way. We can analyze formation structures in two categories: control abstraction and robot distinguishability. The control abstraction can be divided into three layers: formation shape, reference type, and robotic control. Furthermore, robots can be classified as anonymous robots or identification robots depending on whether robots are distinguishable according to their inner states. We use this taxonomy to analyze some ground-based formation systems and to state current challenges of formation control. Such information becomes the design know-how in developing a formation system, and a case study of designing a multi-team formation system is introduced to demonstrate the usefulness of the taxonomy.

Resources like ontologies are used in a number of applications, including natural language processing, information retrieval(especially from the Internet). Different methods have been proposed to build such resources. This paper proposes a new method to extract information from the Web to build a taxonomy of terms and Web resources for a given domain. Firstly, a (CHIR) method is used to identify candidat terms. Then a similarity (SIM) measure is introduced to select relevant concepts to build the ontology. Our new algorithm, called (CHIRSIM), is easy to implement and can be efficiently integrated into an information retrieval system to help improve the retrieval performance. Experimental results show that the proposed approach can effectively and efficiently construct a cancer domain ontology from unstructured text documents.

By 2018, business analytics (BA), believed by global CIOs to be of strategic importance, had for years been their top priority. It is also a focus of academic research, as shown by a large number of papers, books, and research reports. On the other hand, the BA domain suffers from several incorrect, imprecise, and incomplete notions. New areas and concepts emerge quickly; making it difficult to ascertain their structure. BA-related taxonomies play a crucial role in analyzing, classifying, and understanding related objects. However, according to the literature on taxonomy development in information systems (IS), in most cases the process is ad hoc. BA taxonomies and frameworks are available in the literature; however, some are excessively general frameworks with a high-level conceptual focus, while others are application or domain-specific. Our paper aims to present a novel semi-automatic method for taxonomy development and maintenance in the field of BA using content analysis and text mining. The contribution of our research is threefold: (1) the taxonomy development method, (2) the draft taxonomy for BA, and (3) identifying the latest research areas and trends in BA.

This study presents a novel taxonomy of short message service campaigns, for the purpose of building an intelligent marketing system. The main issue of mass marketing is that one size does not fit everybody. In other words, it is challenging to meet different consumer needs. With the help of artificial intelligence, marketers can be supported to overcome some of these challenges. This study uses a mixed methods approach where design science and grounded theory is used to produce a short message service campaign taxonomy for a future intelligent marketing system. Data collection consisted of 386 previously active campaigns used over 33 months to build the taxonomy. An experimental study was conducted to test the effectiveness of the proposed taxonomy. The experiments involved automatic generation of campaign messages. The validity of these campaign messages, and hence the proposed taxonomy, was ascertained by analysing the messages within a business context. The study concludes that the system, intertwined with the taxonomy, performs comparably to a regular campaign. Another proof of concept is that the business context deemed the generated campaign texts to be both semantically and syntactically similar to run them in active campaigns as experiments.

In order to integrate autonomous taxonomy databases in the presence of semantic heterogeneity, we developed and evaluated a new integration algorithm over international computer networks. Whole taxonomy databases in the real applications are encoded in the relational model. First, the algorithm requires users to assign priority according to order of belief for each one of the accessible taxonomy databases. Integration is achieved by increasing the kernel database, selected by the algorithm as the one with the highest priority among the databases. Complex problems are included to recognize consistent parts that can be incrementally added to the kernel databases in the remainder of the accessible taxonomy databases through the use of tree structures. The algorithm with relations operators, was proposed after considering both structural and cooperative constraints useful in managing single and multiple databases. In addition, we present a taxonomy database system including the useful algorithm. The system is also accessible from remote users through WWW (World-Wide Web) and is implemented in SQL programming and CGI (Common Gateway Interface) scripts of WWW.