Supporting adaptiveness of cyber-physical processes through action-based formalisms

Cyber Physical Processes (CPPs) refer to a new generation of business processes enacted in many application environments (e.g., emergency management, smart manufacturing, etc.), in which the presence of Internet-of-Things devices and embedded ICT systems (e.g., smartphones, sensors, actuators) strongly influences the coordination of the real-world entities (e.g., humans, robots, etc.) inhabitating such environments. A Process Management System (PMS) employed for executing CPPs is required to automatically adapt its running processes to anomalous situations and exogenous events by minimising any human intervention. In this paper, we tackle this issue by introducing an approach and an adaptive Cognitive PMS, called SmartPM, which combines process execution monitoring, unanticipated exception detection and automated resolution strategies leveraging on three well-established action-based formalisms developed for reasoning about actions in Artificial Intelligence (AI), including the situation calculus, IndiGolog and automated planning. Interestingly, the use of SmartPM does not require any expertise of the internal working of the AI tools involved in the system

A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

Data Warehousing Modernization: Big Data Technology Implementation

Considering the challenges posed by Big Data, the cost to scale traditional data warehouses is high and the performances would be inadequate to meet the growing needs of the volume, variety and velocity of data. The Hadoop ecosystem answers both of the shortcomings. Hadoop has the ability to store and analyze large data sets in parallel on a distributed environment but cannot replace the existing data warehouses and RDBMS systems due to its own limitations explained in this paper. In this paper, I identify the reasons why many enterprises fail and struggle to adapt to Big Data technologies. A brief outline of two different technologies to handle Big Data will be presented in this paper: Using IBM’s Pure Data system for analytics (Netezza) usually used in reporting, and Hadoop with Hive which is used in analytics. Also, this paper covers the Enterprise architecture consisting of Hadoop that successful companies are adapting to analyze, filter, process, and store the data running along a massively parallel processing data warehouse. Despite, having the technology to support and process Big Data, industries are still struggling to meet their goals due to the lack of skilled personnel to study and analyze the data, in short data scientists and data statisticians

Enabling data-driven decision-making for a Finnish SME: a data lake solution

In the era of big data, data-driven decision-making has become a key success factor for companies of all sizes. Technological development has made it possible to store, process and analyse vast amounts of data effectively. The availability of cloud computing services has lowered the costs of data analysis. Even small businesses have access to advanced technical solutions, such as data lakes and machine learning applications. Data-driven decision-making requires integrating relevant data from various sources. Data has to be extracted from distributed internal and external systems and stored into a centralised system that enables processing and analysing it for meaningful insights. Data can be structured, semi-structured or unstructured. Data lakes have emerged as a solution for storing vast amounts of data, including a growing amount of unstructured data, in a cost-effective manner. The rise of the SaaS model has led to companies abandoning on-premise software. This blurs the line between internal and external data as the company’s own data is actually maintained by a third-party. Most enterprise software targeted for small businesses are provided through the SaaS model. Small businesses are facing the challenge of adopting data-driven decision-making, while having limited visibility to their own data. In this thesis, we study how small businesses can take advantage of data-driven decision-making by leveraging cloud computing services. We found that the report- ing features of SaaS based business applications used by our case company, a sales oriented SME, were insufficient for detailed analysis. Data-driven decision-making required aggregating data from multiple systems, causing excessive manual labour. A cloud based data lake solution was found to be a cost-effective solution for creating a centralised repository and automated data integration. It enabled management to visualise customer and sales data and to assess the effectiveness of marketing efforts. Better skills at data analysis among the managers of the case company would have been detrimental to obtaining the full benefits of the solution

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

The Data Lakehouse: Data Warehousing and More

Relational Database Management Systems designed for Online Analytical Processing (RDBMS-OLAP) have been foundational to democratizing data and enabling analytical use cases such as business intelligence and reporting for many years. However, RDBMS-OLAP systems present some well-known challenges. They are primarily optimized only for relational workloads, lead to proliferation of data copies which can become unmanageable, and since the data is stored in proprietary formats, it can lead to vendor lock-in, restricting access to engines, tools, and capabilities beyond what the vendor offers. As the demand for data-driven decision making surges, the need for a more robust data architecture to address these challenges becomes ever more critical. Cloud data lakes have addressed some of the shortcomings of RDBMS-OLAP systems, but they present their own set of challenges. More recently, organizations have often followed a two-tier architectural approach to take advantage of both these platforms, leveraging both cloud data lakes and RDBMS-OLAP systems. However, this approach brings additional challenges, complexities, and overhead. This paper discusses how a data lakehouse, a new architectural approach, achieves the same benefits of an RDBMS-OLAP and cloud data lake combined, while also providing additional advantages. We take today's data warehousing and break it down into implementation independent components, capabilities, and practices. We then take these aspects and show how a lakehouse architecture satisfies them. Then, we go a step further and discuss what additional capabilities and benefits a lakehouse architecture provides over an RDBMS-OLAP

Business intelligence-centered software as the main driver to migrate from spreadsheet-based analytics

Internship Report presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceNowadays, companies are handling and managing data in a way that they weren’t ten years ago. The data deluge is, as a mere consequence of that, the constant day-to-day challenge for them - having to create agile and scalable data solutions to tackle this reality. The main trigger of this project was to support the decision-making process of a customer-centered marketing team (called Customer Voice) in the Company X by developing a complete, holistic Business Intelligence solution that goes all the way from ETL processes to data visualizations based on that team’s business needs. Having this context into consideration, the focus of the internship was to make use of BI, ETL techniques to migrate their data stored in spreadsheets — where they performed data analysis — and shift the way they see the data into a more dynamic, sophisticated, and suitable way in order to help them make data-driven strategic decisions. To ensure that there was credibility throughout the development of this project and its subsequent solution, it was necessary to make an exhaustive literature review to help me frame this project in a more realistic and logical way. That being said, this report made use of scientific literature that explained the evolution of the ETL workflows, tools, and limitations across different time periods and generations, how it was transformed from manual to real-time data tasks together with data warehouses, the importance of data quality and, finally, the relevance of ETL processes optimization and new ways of approaching data integrations by using modern, cloud architectures

CLINICAL DATA WAREHOUSE: A REVIEW

Clinical decisions are crucial because they are related to human lives. Thus, managers and decision makers inthe clinical environment seek new solutions that can support their decisions. A clinical data warehouse (CDW) is animportant solution that is used to achieve clinical stakeholders’ goals by merging heterogeneous data sources in a centralrepository and using this repository to find answers related to the strategic clinical domain, thereby supporting clinicaldecisions. CDW implementation faces numerous obstacles, starting with the data sources and ending with the tools thatview the clinical information. This paper presents a systematic overview of purpose of CDWs as well as the characteristics;requirements; data sources; extract, transform and load (ETL) process; security and privacy concerns; design approach;architecture; and challenges and difficulties related to implementing a successful CDW. PubMed and Google Scholarare used to find papers related to CDW. Among the total of 784 papers, only 42 are included in the literature review. Thesepapers are classified based on five perspectives, namely methodology, data, system, ETL tool and purpose, to findinsights related to aspects of CDW. This review can contribute answers to questions related to CDW and providerecommendations for implementing a successful CDW