Managed ecosystems of networked objects

Small embedded devices such as sensors and actuators will become the cornerstone of the Future Internet. To this end, generic, open and secure communication and service platforms are needed in order to be able to exploit the new business opportunities these devices bring. In this paper, we evaluate the current efforts to integrate sensors and actuators into the Internet and identify the limitations at the level of cooperation of these Internet-connected objects and the possible intelligence at the end points. As a solution, we propose the concept of Managed Ecosystem of Networked Objects, which aims to create a smart network architecture for groups of Internet-connected objects by combining network virtualization and clean-slate end-to-end protocol design. The concept maps to many real-life scenarios and should empower application developers to use sensor data in an easy and natural way. At the same time, the concept introduces many new challenging research problems, but their realization could offer a meaningful contribution to the realization of the Internet of Things

Active networks: an evolution of the internet

Active Networks can be seen as an evolution of the classical model of packet-switched networks. The traditional and ”passive” network model is based on a static definition of the network node behaviour. Active Networks propose an “active” model where the intermediate nodes (switches and routers) can load and execute user code contained in the data units (packets). Active Networks are a programmable network model, where bandwidth and computation are both considered shared network resources. This approach opens up new interesting research fields. This paper gives a short introduction of Active Networks, discusses the advantages they introduce and presents the research advances in this field

Reinventing discovery learning: a field-wide research program

© 2017, Springer Science+Business Media B.V., part of Springer Nature. Whereas some educational designers believe that students should learn new concepts through explorative problem solving within dedicated environments that constrain key parameters of their search and then support their progressive appropriation of empowering disciplinary forms, others are critical of the ultimate efficacy of this discovery-based pedagogical philosophy, citing an inherent structural challenge of students constructing historically achieved conceptual structures from their ingenuous notions. This special issue presents six educational research projects that, while adhering to principles of discovery-based learning, are motivated by complementary philosophical stances and theoretical constructs. The editorial introduction frames the set of projects as collectively exemplifying the viability and breadth of discovery-based learning, even as these projects: (a) put to work a span of design heuristics, such as productive failure, surfacing implicit know-how, playing epistemic games, problem posing, or participatory simulation activities; (b) vary in their target content and skills, including building electric circuits, solving algebra problems, driving safely in traffic jams, and performing martial-arts maneuvers; and (c) employ different media, such as interactive computer-based modules for constructing models of scientific phenomena or mathematical problem situations, networked classroom collective “video games,” and intercorporeal master–student training practices. The authors of these papers consider the potential generativity of their design heuristics across domains and contexts

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

Supply Chain Digitalization – Optimizing The Factory Material Flow

Nykypäivän kiristynyt kilpailu ja edistyneet toimitusketjut edellyttävät entistä parempaa tomistusketjun digitalisointia parantamaan tehokkuutta, toimistusvarmuutta ja yrityksen kannattavuutta. Toimitusketjun digitalisoinnin avulla voidaan tukea reaaliaikaisesti muuttuvaa kysyntää valmistusyksikössä, luomalla älykäs toimitusketju joka tukee kysyntälähtöistä arvoketjua. Tämän tutkimuksen tarkoituksena on selvittää ja esittää kohdeyritykselle mahdolliset toiminallisuudet ja tuotteet SAP S/4HANA:sta jotka tukevat kohdeyrityksen nykyisiä ja suunniteltuja toimitusketjun prosesseja. Työssä tutkitaan hankinnasta, varastonhallintaan ja valmistukseen liittyviä toiminallisuuksia. Toiminnallisuudet analysoidaan kohdeyrityksen näkökulmasta ja verrataan vaatimuksiin, joita kohdeyrityksessa nostetaan esille. Tutkielman teoreettinen osuus pohjautuu toimitusketjun ja ERP SAP uuteen tuotteeseen SAP S/4HANA. Toimitusketjun osuudessa tuodaan esille, kuinka vaatimukset Toimitusketju 4.0 ja Teollisuus 4.0 voidaan nähdä suuntaviivoina SAP S/4HANA:n kehitykselle. Empiirinen osuus jakautuu kahteen tutkimuskysymykseen: Mitkä ovat tulevaisuuden mahdollisuudet visualisoida, arvioida ja optimoida toimitusketjua SAP S/4HANA ydintoiminnoilla? Mitä hyötyä kohde-yritys voi saavuttaa ottamalla käyttöön SAP S/4HANA laajennetut toiminallisuudet? Työn tuloksena saatiin ymmärrys SAP S/4HANA tuotteesta ja sen mahdollisuuksista toimitusketjun osalta. SAP S/4HANA tuotteena tukee monia kohdeyrityksen vaatimuksia ylätasolla, mutta vaatii konkreettisempia tutkimuksia ja testausta kohdeyrityksen työskentely ympäristössä sekä pitäisi hyödyntää kohdeyrityksen dataa.Today’s increasing competition and advanced supply chain needs better digitized supply chain to improve efficiency, on time delivery and business profitability. Supply chain digitalization enable to have real- time optimized supply chain. Supply chain management supporting the variable manufacturing unit demand by creating smart supply chain that supports the demand-driven value chain. The purpose of the study is to investigate and present how the SAP S/4HANA functionalities and products could support the case company and defined supply chain processes. The study explores supply chain from purchasing, inventory management to manufacturing. Functionalities are analysed from case company perspective and are compared to requirements what case company has raised up. Theoretical chapters focus on the supply chain and ERP SAP new product SAP S/4HANA. Supply chain chapter is divided to Supply 4.0 and Industry 4.0 to bring up the view how the SAP S/4HANA is aligned with these topics. Empirical part is divided into two research questions: What are the future capabilities to visualize, evaluate and optimize the supply chain including the core functionalities of the SAP S/4HANA? What benefits the case company can achieve by implementing the extended functionalities of the SAP S/4HANA? As a result of the study a good understanding of the SAP S/4HANA product and the possibility related to supply chain was reached. SAP S/4HANA as a product supports most of the requirements from case company in high level but requires more specific research and testing in company environment with real company data