Linked data paradigm for enterprises: information integration and value chain
Data integration in enterprises is a crucial but at the same time costly and challenging problem. While business-critical information is stored in ERP, CRM, SCM and in Content Management systems the integration of such becomes even more critical when integrating with the growing information space on the Web. The IT industry has developed over the last decade integration solutions based on Master Data Management, Business Intelligence and the Service Oriented Architecture. However, we become increasingly aware that such technologies are not sufficient to ultimately solve all data integration challenges. Under the vision of context-aware services and integration we propose to apply the technology of the Linked Data paradigm. This approach seems to be promising, as scientists in the evolution of the Semantic Web have used it. We discuss Linked Data approaches in relation to the value chain and information integration of heterogeneous content and present an example of a CRM business process applying the Linked Data principles.
The article presents the key aspects of successful investment process modeling for investment projects. A successful investment process means a series of investment decisions on timing, risk and investment objects and activities for their implementation, aimed to generating positive indicator "alpha", which is the maximum possible total return of specific investment project. In practice, there is no unique investment process, applicable to any investment decision, however, there are basic requirements that must be met to ensure a successful outcome of investment process. These requirements include availability of investment opportunities, forecasting skills and mechanism for investment project implementation.
This book constitutes the refereed proceedings of the 6th Conference on Knowledge Engineering and the Semantic Web, KESW 2015, held in Moscow, Russia, in September/October 2015. The 17 revised full papers presented together with 6 short system descriptions were carefully reviewed and selected from 35 submissions. The papers address research issues related to semantic web, linked data, ontologies, natural language processing, knowledge representation.
To large organizations, business intelligence (BI) promises the capability of collecting and analyzing internal and external data to generate knowledge and value, thus providing decision support at the strategic, tactical, and operational levels. BI is now impacted by the “Big Data” phenomena and the evolution of society and users. In particular, BI applications must cope with additional heterogeneous (often Web-based) sources, e.g., from social networks, blogs, competitors’, suppliers’, or distributors’ data, governmental or NGO-based analysis and papers, or from research publications. In addition, they must be able to provide their results also on mobile devices, taking into account location-based or time-based environmental data. The lectures held at the Third European Business Intelligence Summer School (eBISS), which are presented here in an extended and refined format, cover not only established BI and BPM technologies, but extend into innovative aspects that are important in this new environment and for novel applications, e.g., pattern and process mining, business semantics, Linked Open Data, and large-scale data management and analysis. Combining papers by leading researchers in the field, this volume equips the reader with the state-of-the-art background necessary for creating the future of BI. It also provides the reader with an excellent basis and many pointers for further research in this growing field.
The article describes an approach to the metadata inclusion into Open XML and ODF documents. This metadata allows implement semantic indexing. The described solution is realized as a software library SemanticLib that provides a uniform access to documents in these formats.
Multi- and interdisciplinary nature of the supply chain management and modern logistic strategic potential define logistical context of a business model.
Some implications have been made based on the reviewed articles about logistics and business models. (1) It is evident that authors have little agreement about logistical challenges in business models that can be explained by lack of unity in understanding the business model concept. (2) Logistics and business models are interrelated. Logistics influences efficiency, results and possibilities of a business model development. Moreover, a business model defines requirements for logistics. (3) Significance of logistics in a business model is determined by the object of logistics which is an aggregate of interrelated material, information and financial flows. The system of material flows ensures management of information flows in the value chain. This in turn is a basis of fair distribution of financial results between supply chain partners.
The author uses a consolidated definition of a business model as an object that deals with assessment, creation, distribution and supply of value to the client, and also with allocation of profit collected thanks to its acknowledgement on the market. The researcher attempts to update logistics as an instrument of developing and applying a business model. It is emphasized that it has an unquestionable primary role in the chain to create, distribute and supply value to the client.
The author suggests and describes the model of a logistics business model and develops a system of indicators to evaluate and analyze logistics in a business model. The system of indicators can be used to complete two tasks. The first task is to evaluate logistical component of a business model to develop logistical strategy in a company. The second task is to analyze the results of logistical activity in the framework of a business model to understand the rationale to develop a new business model in a company.
This paper proposes a conceptual approach to take into account both short-and long-term effects of marketing activities in enterprises across the value chain and developed a set if new indicators that allow for the analysis of the contribution of companies involved in creating value for customers in the aggregate terms across the value chain. This paper integrates the concepts of network relations, value chain management and inter-firm marketing effort that focus on customer orientation. Based on this conceptual basis we propose a sequence of actions that can translate these concepts into measures and indicators that allow a firm to understand their role in creating sustainable value. These measures are then validated through the analysis of customer orientation marketing approach.
This book constitutes the refereed proceedings of the 4th Conference on Knowledge Engineering and the Semantic Web, KESW 2013, held in St. Petersburg, Russia, in October 2013. The 18 revised full papers presented together with 7 short system descriptions were carefully reviewed and selected from 52 submissions. The papers address research issues related to knowledge representation, semantic web, and linked data.
I give the explicit formula for the (set-theoretical) system of Resultants of m+1 homogeneous polynomials in n+1 variables