Business Incubators: Creation of a Fit in Armenia

In this paper, we evaluate the extent to which business incubation services meet tenant’s needs. Additionally, we pose the question of whether the current business incubators actually cover the needs of a particular industry. Our empirical setting is a developing country in the Caucasian Region (Armenia) and we chose to research solely the IT industry. We employed a two stage procedure: first, we conducted interviews with pivotal people familiar with business incubation in Armenia; second, an electronic questionnaire survey was sent to the entire Armenian IT population. The results suggest a moderate need of IT companies for the typical business incubation services. Further, we show that incubated companies are generally satisfied with the services they enjoy albeit this satisfaction level decreases as the needs increase. Non-incubated companies, on the other hand, perceive incubation services to be valuable for their development and this value increases when their needs increase. Our study implies that a more extensive service provision is necessary to fully cover the needs of the Armenian IT industry for business incubation services

An Analysis of Service Ontologies

Services are increasingly shaping the world’s economic activity. Service provision and consumption have been profiting from advances in ICT, but the decentralization and heterogeneity of the involved service entities still pose engineering challenges. One of these challenges is to achieve semantic interoperability among these autonomous entities. Semantic web technology aims at addressing this challenge on a large scale, and has matured over the last years. This is evident from the various efforts reported in the literature in which service knowledge is represented in terms of ontologies developed either in individual research projects or in standardization bodies. This paper aims at analyzing the most relevant service ontologies available today for their suitability to cope with the service semantic interoperability challenge. We take the vision of the Internet of Services (IoS) as our motivation to identify the requirements for service ontologies. We adopt a formal approach to ontology design and evaluation in our analysis. We start by defining informal competency questions derived from a motivating scenario, and we identify relevant concepts and properties in service ontologies that match the formal ontological representation of these questions. We analyze the service ontologies with our concepts and questions, so that each ontology is positioned and evaluated according to its utility. The gaps we identify as the result of our analysis provide an indication of open challenges and future work

DevOps Ontology - An ontology to support the understanding of DevOps in the academy and the software industry

Currently, the degree of knowledge about what DevOps really means and what it entails is still limited. This can result in an informal and even incorrect implementation in many cases. Although several proposals related to DevOps adoption can be found, confusion is not uncommon and terminology conflict between the proposals is still evident. This article proposes DevOps Ontology, a semi-formal ontology that proposes a generic, consistent, and clear language to enable the dissemination of information related to implementing DevOps in software development. The ontology presented in this article facilitates the understanding of DevOps by identifying the relationships between software process elements and the agile principles/values that may be related to them. The DevOps Ontology has been defined considering the following aspects: the REFSENO formalism that uses the representation in UML was used and the language OWL language using Prótegé and HermiT Reasoner to evaluate the consistency of its structure. Likewise, it was satisfactorily evaluated in three application cases: a theoretical validation; instantiation of the continuous integration and deployment practices proposed by the company GitLab. Furthermore, a mobile app was created to retrieve information from the DevOps Ontology using the SPARQL protocol and RDF language. The app also evaluated the Ontology’s proficiency in responding to knowledge-based questions using SPARQL. The results showed that DevOps Ontology is consistent, complete, and concise, i.e.: to say: the consistency could be observed in the ability to be able to infer knowledge from the ontology, ensuring that the ontology is complete by checking for any incompleteness and verifying that all necessary definitions and inferences are well-established. Additionally, the ontology was assessed for conciseness to ensure that it doesn't contain redundant or unnecessary definitions. Furthermore, it has the potential for improvement by incorporating new concepts and relationships as needed. The newly suggested ontology creates a set of terms that provide a systematic and structured approach to organizing the existing knowledge in the field. This helps to minimize the confusion, inconsistency, and heterogeneity of the terminologies and concepts in the area of interest

Natural Language Interfaces to Data

Recent advances in NLU and NLP have resulted in renewed interest in natural language interfaces to data, which provide an easy mechanism for non-technical users to access and query the data. While early systems evolved from keyword search and focused on simple factual queries, the complexity of both the input sentences as well as the generated SQL queries has evolved over time. More recently, there has also been a lot of focus on using conversational interfaces for data analytics, empowering a line of non-technical users with quick insights into the data. There are three main challenges in natural language querying (NLQ): (1) identifying the entities involved in the user utterance, (2) connecting the different entities in a meaningful way over the underlying data source to interpret user intents, and (3) generating a structured query in the form of SQL or SPARQL. There are two main approaches for interpreting a user's NLQ. Rule-based systems make use of semantic indices, ontologies, and KGs to identify the entities in the query, understand the intended relationships between those entities, and utilize grammars to generate the target queries. With the advances in deep learning (DL)-based language models, there have been many text-to-SQL approaches that try to interpret the query holistically using DL models. Hybrid approaches that utilize both rule-based techniques as well as DL models are also emerging by combining the strengths of both approaches. Conversational interfaces are the next natural step to one-shot NLQ by exploiting query context between multiple turns of conversation for disambiguation. In this article, we review the background technologies that are used in natural language interfaces, and survey the different approaches to NLQ. We also describe conversational interfaces for data analytics and discuss several benchmarks used for NLQ research and evaluation.Comment: The full version of this manuscript, as published by Foundations and Trends in Databases, is available at http://dx.doi.org/10.1561/190000007

A Classification of Motivation and Behavior Change Techniques Used in Self-Determination Theory-Based Interventions in Health Contexts

While evidence suggests that interventions based on self-determination theory have efficacy in motivating adoption and maintenance of health-related behaviors, and in promoting adaptive psychological outcomes, the motivational techniques that comprise the content of these interventions have not been comprehensively identified or described. The aim of the present study was to develop a classification system of the techniques that comprise self-determination theory interventions, with satisfaction of psychological needs as an organizing principle. Candidate techniques were identified through a comprehensive review of self-determination theory interventions and nomination by experts. The study team developed a preliminary list of candidate techniques accompanied by labels, definitions, and function descriptions of each. Each technique was aligned with the most closely-related psychological need satisfaction construct (autonomy, competence, or relatedness). Using an iterative expert consensus procedure, participating experts (N=18) judged each technique on the preliminary list for redundancy, essentiality, uniqueness, and the proposed link between the technique and basic psychological need. The procedure produced a final classification of 21 motivation and behavior change techniques (MBCTs). Redundancies between final MBCTs against techniques from existing behavior change technique taxonomies were also checked. The classification system is the first formal attempt to systematize self-determination theory intervention techniques. The classification is expected to enhance consistency in descriptions of selfdetermination theory-based interventions in health contexts, and assist in facilitating synthesis of evidence on interventions based on the theory. The classification is also expected to guide future efforts to identify, describe, and classify the techniques that comprise self-determination theory-based interventions in multiple domains