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All organizations big or small need to manage their data effectively, both the structured data stored in operational databases and the unstructured data that is spread out across documents. As the data size grows, it becomes essential to move the data to a warehouse so that data is available for adhoc queries and analysis. This data repository of both useful data and documents along with meta data...
As a member of the European Union, Hungary face different challenges, of which the most important are the transformation of the healthcare system, the Social Security and pension system and the system of taxation. These economic and social challenges require long-term governmental strategies, which should be modelled, tested, verified in some way. It is for this challenge that we find an efficient...
OpenSpending is the world's largest repository of open fiscal datasets. As of now it only supported loading datasets from CSV files, leaving out more heavily-structured formats like RDF. Rudolf, an RDF backend has been developed to expose Data Cube RDF triples and OLAP operations in JSON format through an OpenSpending-compatible HTTP API. This API can be directly utilized by the OpenSpending Viewer...
This report describes the challenges and experiences with the incremental migration of a BPEL to a BPMN 2.0 process engine. The transition is motivated by a strategic reorientation towards the new standard as well as end of life of the previous product. The solution reflects the preliminary steps of integrating the new platform into the existing application and support for parallel operation. This...
Data loss, i.e. the unauthorized/unwanted disclosure of data, is a major threat for modern organizations. Data Loss Protection (DLP) solutions in use nowadays, either employ patterns of known attacks (signature-based) or try to find deviations from normal behavior (anomaly-based). While signature-based solutions provide accurate identification of known attacks and, thus, are suitable for the prevention...
The objective of this paper is to present a review literature on what are impacts of Data Mining (DM) in Business Intelligence (BI). The paper highlights various features of DM. It involves three steps: explorations, pattern identification and deployment. BI is the hot topic among all industries aiming for relevance. BI emphasizes on detail integration and or organizing of data. DM and BI work together...
Computational Maritime Situational Awareness (MSA) supports the maritime industry, governments, and international organizations with machine learning and big data techniques for analyzing vessel traffic data available through the Automatic Identification System (AIS). A critical challenge of scaling computational MSA to big data regimes is integrating the core learning algorithms with big data storage...
The disclosure of sensitive data to unauthorized entities is a critical issue for organizations. Timely detection of data leakage is crucial to reduce possible damages. Therefore, breaches should be detected as early as possible, e.g., when data are leaving the database. In this paper, we focus on data leakage detection by monitoring database activities. We present a framework that automatically learns...
Nowadays Big Data becomes one of the biggest buzz concepts in IT world especially with the vertiginous development driving the increase of data encouraged by the emergence of high technologies of storage like cloud computing. Big Data can create efficient challenging solutions in health, security, government and more; and usher in a new era of analytics and decisions. Knowledge Management comprises...
Developed a mathematical model and a functional schemes for workflow systems with protection information. The system is based on the role security model and provides multiple levels of attributes for documents. The developed system ensures the availability, integrity and confidentiality of information, protecting documents from tampering. For printed documents developed several levels of protection,...
The nature of operational data that is frequently updated, and the interest to reflect business environment alterations in the business analysis process to make operational decisions, need more than the typical data warehousing methodology for data integration. In this paper, therefore, we propose a new methodology to dynamically extract real-time data from various internal and external data sources...
Integration of data from heterogeneous data sources is one of several technical challenges during the tunnel construction. BIM and relational database have maken their efforts for tunnel data organization, but they are not qualified for tunnel problem reasoning. This paper proposes a tunnel data organization method driven by Ontology, which is suitable for data reorganization as well as reasoning...
Multi-tenancy architecture (MTA) is often used in Software-as-a-Service (SaaS) and the central idea is that multiple tenant applications can be developed using components stored in the SaaS infrastructure. Recently, MTA has been extended where a tenant application can have its own sub-tenants as the tenant application acts like a SaaS infrastructure. In other words, MTA is extended to STA (Sub-Tenancy...
Humanitarian aid efforts in response to natural and man-made disasters often involve complicated logistical challenges. Problems such as communication failures, damaged infrastructure, violence, looting, and corrupt officials are examples of obstacles that aid organizations face. The inability to plan relief operations during disaster situations leads to greater human suffering and wasted resources...
The objective of this paper is to explain the appropriately database partitions and accessing the required of the deterministic data in the form of streams. The part of data bases is maintained for a variety and a number of manageable and performance related reasons. As a whole there must be a balance among the various requirements. It was observed in so many numbers of organizations, that particularity...
Existing enterprise management information system database design commonly uses relational database management mode. This organization within the enterprise rich and diverse types of data is simplified as a two-dimensional table. This approach has limitation. In this paper, for the data organization, we propose to use category theory. Taking data as an object, by defining data relationships through...
In order to succeed in the global competition, organizations need to understand and monitor customers' behavior, so that they could retain them by predicting their preference and behavior before others. Recently, marketing strategies have been changed from product-oriented strategies to customer-oriented strategies and most organizations have focused on customer relationship management. In fact, more...
This paper describes the meta-data warehouse of Credit Suisse that is productive since 2009. Like most other large organizations, Credit Suisse has a complex application landscape and several data warehouses in order to meet the information needs of its users. The problem addressed by the meta-data warehouse is to increase the agility and flexibility of the organization with regards to changes such...
In this abridged version, which is a summary of the authors' study, we present some of our findings for consideration. The principal objective of the study was to investigate empirically the architectures of data warehouse (DW), or more specifically, the types of the architectures and a number of factors, which are believed to influence their selection, were explored. A questionnaire survey, which...
In order to insure the quality of battlefield information guarantee, promote efficiency of data management, and mining these values of data, a data management approach for battlefield data of targets and environment is studied in this paper. Concepts of targets and environment are introduced, these originals of data are discussed and classified, and these characters of data are analyzed. By describing...
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