Tahapan Pengembangan Digital Dashboard Sebagai Tools Enterprise Performance Monitoring

Paper ini membahas tahapan pengembangan model digital dashboard (DD) sebagai tools enterprise performance management (EPM). Bertujuan untuk mengetahui sejauh mana model DD dikembangkan untuk mempermudah pengaksesan informasi strategis, melaksanakan monitoring, evaluasi, dan pengukuran kinerja enterprise. Penelitian juga bermaksud untuk mengidenfikasi apakah komponen critical suscess factor (CSFs), business process (BP), business activity monitoring (BAM), key performance indicator (KPI), mapping BAM ke dalam KPI telah dilakukan dalam pengembangan digital dashboard. Penelitian dilakukan dengan cara mengidenfikasi tahapan membangun EPM-DD, bentuk EPM-DD, komponen EPM-DD, cakupan EPM-DD, membuat tabel komparasi, dan menganalisanya. Hasil penelitian menyatakan bahwa tahapan pengembangan digital dashboard tediri dari: menentukan metodologi, mendefinisikan data layer, presentation layer, visualization layer, menentukan komponen, dan cakupan data dan user. Diketahui pula bahwa komponen BP, BAM, KPI telah diperhatikan pada beberapa pengembangan. Sementara komponen CSFs sama sekali belum menjadi perhatikan. Telah dibuat pula sebuah rekomendasi tahapan pengembangan digital dashboard sebagai tools EPM yang memiliki 6 tahapan

Personal Learning Environments for Inquiry-Based Learning

Personal Learning Environments have recently emerged as a novel approach to learning, putting learners in the spotlight and providing them with the tools for building their own learning environments according to their specific learning needs and aspirations. This approach enables learners to take complete control over their learning, thus becoming self-regulated and independent. This paper introduces a new European initiative for supporting and enhancing inquiry-based learning through Personal Learning Environments consisting of personal and social inquiry tools. This approach aims at supporting students in developing their self-regulated learning skills by conducting their scientific inquiries in collaboration with their peers

Participatory aid marketplace : designing online channels for digital humanitarians

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 215-236).Recent years have seen an increase in natural and man-made crises. Information and communication technologies are enabling citizens to contribute creative solutions and participate in crisis response in myriad new ways, but coordination of participatory aid projects remains an unsolved challenge. I present a wide-ranging case library of creative participatory aid responses and a framework to support investigation of this space. I then co-design a Marketplace platform with leading Volunteer & Technical Communities to aggregate participatory aid projects, connect skilled volunteers with relevant ways to help, and prevent fragmentation of efforts. The result is a prototype to support the growth of participatory aid, and a case library to improve understanding of the space. As the networked public takes a more active role in its recovery from crisis, this work will help guide the way forward with specific designs and general guidelines.by Matt Stempeck.S.M

Using interpretable machine learning for indoor CO₂ level prediction and occupancy estimation

Management and monitoring of rooms’ environmental conditions is a good step towards achieving energy efficiency and a healthy indoor environment. However, studies indicate that some of the current methods used in environmental room monitoring are faced with some challenges such as high cost and lack of privacy. As a result, there is need to use a method that is simpler, reliable, affordable and without any privacy issues. Therefore, the aims of this thesis were: (i) to predict future CO₂ levels using environmental sensor data, (ii) to determine room occupancy using environmental sensor data and (iii) to create a prototype dashboard for possible future room management based on the models developed for room occupancy and CO₂ prediction. Machine learning methods were used and these included: Gradient Boosting ensemble model (GB), Long Short-Term Memory recurrent neural network model (LSTM) and Facebook Prophet model for time series (Prophet). The sensor data were recorded from three different office locations (two test sites at a university and a real-world commercial office in Glasgow, Scotland, UK). The results of the analysis show that with LSTM method, a Root Mean Square Error (RMSE) (absolute fit of the model results to the observed data) of 0.0682 could be achieved for two-hour time interval CO₂ prediction and with GB, of 82% accuracy could be achieved for proposed room occupancy estimation. Furthermore, as the model understanding was raised as a key issue, interpretable machine learning methods (SHapley Additive exPlanation. (SHAP) and Local Model-agnostic explanations. (LIME)) were used to interpret room occupancy results obtained by GB model. In addition a dashboard was designed and prototyped to show room environmental data, predicted CO₂ levels and estimated room occupancy based on what the sensor data and models might provide for people managing rooms in different settings. The proposed dashboard that was designed in this research was evaluated by interested participants and their responses show that the proposed dashboard could potentially offer inputs to building management towards the control of heating, ventilation and air-conditioning systems. This in turn could lead to improved energy efficiency, better planning of shared spaces in buildings, potentially reducing energy and operational costs, improved environmental conditions for room occupants; potentially leading to improved health, reduced risks, enhanced comfort and improved productivity. It is advised that further studies should be conducted at multiple locations to demonstrate generalisation of the results of the proposed model. In addition, the end benefits of the model could be assessed through applying its outputs to enhance the control of HVAC systems, room management systems and safety systems. The health and productivity of the occupants could be monitored in detail to identify whether resulting environmental improvements deliver improvements in health and productivity. The findings of this research contribute new knowledge that could be used to achieve reliable results in room occupancy estimation using machine learning approach.Management and monitoring of rooms’ environmental conditions is a good step towards achieving energy efficiency and a healthy indoor environment. However, studies indicate that some of the current methods used in environmental room monitoring are faced with some challenges such as high cost and lack of privacy. As a result, there is need to use a method that is simpler, reliable, affordable and without any privacy issues. Therefore, the aims of this thesis were: (i) to predict future CO₂ levels using environmental sensor data, (ii) to determine room occupancy using environmental sensor data and (iii) to create a prototype dashboard for possible future room management based on the models developed for room occupancy and CO₂ prediction. Machine learning methods were used and these included: Gradient Boosting ensemble model (GB), Long Short-Term Memory recurrent neural network model (LSTM) and Facebook Prophet model for time series (Prophet). The sensor data were recorded from three different office locations (two test sites at a university and a real-world commercial office in Glasgow, Scotland, UK). The results of the analysis show that with LSTM method, a Root Mean Square Error (RMSE) (absolute fit of the model results to the observed data) of 0.0682 could be achieved for two-hour time interval CO₂ prediction and with GB, of 82% accuracy could be achieved for proposed room occupancy estimation. Furthermore, as the model understanding was raised as a key issue, interpretable machine learning methods (SHapley Additive exPlanation. (SHAP) and Local Model-agnostic explanations. (LIME)) were used to interpret room occupancy results obtained by GB model. In addition a dashboard was designed and prototyped to show room environmental data, predicted CO₂ levels and estimated room occupancy based on what the sensor data and models might provide for people managing rooms in different settings. The proposed dashboard that was designed in this research was evaluated by interested participants and their responses show that the proposed dashboard could potentially offer inputs to building management towards the control of heating, ventilation and air-conditioning systems. This in turn could lead to improved energy efficiency, better planning of shared spaces in buildings, potentially reducing energy and operational costs, improved environmental conditions for room occupants; potentially leading to improved health, reduced risks, enhanced comfort and improved productivity. It is advised that further studies should be conducted at multiple locations to demonstrate generalisation of the results of the proposed model. In addition, the end benefits of the model could be assessed through applying its outputs to enhance the control of HVAC systems, room management systems and safety systems. The health and productivity of the occupants could be monitored in detail to identify whether resulting environmental improvements deliver improvements in health and productivity. The findings of this research contribute new knowledge that could be used to achieve reliable results in room occupancy estimation using machine learning approach

The Structure and Components for the Open Education Ecosystem - Constructive Design Research of Online Learning Tools

This research studies the design of online learning tools for open education. The dissertation is based on five articles and design case studies that explore open education from different perspectives: open educational resources, open learning environments, and assessment of teachers' competencies. The underlying concept of the study is the open education ecosystem. The study explores the ways in which the design of online learning tools could benefit from the digital ecosystems approach. The design of online learning tools for open education presents wicked problems, that involve ill-defined requirements and contemplates the influence on and by the stakeholders and other components of the ecosystem. Firstly, to clarify the design challenges related to the open education ecosystem, this study summarizes a set of design challenges presented in design case studies. Secondly, it identifies and recommends a set of design patterns that address these design challenges. Finally, the study proposes the structure and components that are needed for the open education ecosystem

Utilization of industrial internet in maintenance reports and information products

Koneellistumisen ja automaation myötä sitoutunut pääoma laitteissa ja tuotantolaitoksissa on lisääntynyt. Tehokas kunnossapidon ohjaus ja hallinta on täten elintärkeää kannattavan liiketoiminnan ja laitteiden hyvän käyntivarmuuden saavuttamiseksi. Kunnossapidon hallinnassa kustannuksia pyritään minimoimaan tehokkaalla töiden ja resurssien ohjauksella tavoitellen mahdollisimman korkeaa käyttöastetta. Kunnossapidon prosesseja ohjataan toiminnanohjausjärjestelmien ja tietotuotteiden avulla. Tiedon määrän lisääntyessä tietotuotteiden rooli on yhä tärkeämmässä roolissa, jotta teknologian ja teollisen internetin hyödyt pystytään realisoimaan. Teollisen internetin ympäristössä tietotuotteet sisältävät toiminnanohjausjärjestelmien ja analyysimallien lisäksi reaaliaikaista tietoa laitteiden antureista. Haasteena on korkealle jalostetun tiedon esittäminen loppukäyttäjälle mahdollisimman relevantissa ja ymmärrettävässä muodossa. Työssä selvitettiin, minkälainen kunnossapidon tietotuote palvelee sen käyttäjiään parhaiten teollisen internetin ympäristössä. Alatutkimuskysymyksien avulla selvitettiin, miten tällaisen tietotuotteen avulla pystytään vaikuttamaan yrityksen kunnossapitoon ja miten tällainen tietotuote palvelee päätöksenteon ja työnteon tukena sekä johtajan että kunnossapitotyöntekijän näkökulmasta. Työn kohdeyrityksenä toimi IFS:n tytäryhtiö MainIoT Software Oy. Kirjallisuuskatsauksen avulla luotiin olemassa olevan teorian pohjalta teoreettinen tietotuotekonsepti. Teoreettiseen tietotuotekonseptiin pyrittiin kokoamaan mahdollisimman kattavasti tietotuotteeseen liittyviä huomioita. Tutkimuksen empiirisessä osassa pyrittiin haastattelujen avulla täydentämään tietotuotekonseptia hyödyntäen eri sidosryhmiä. Haastatteluja toteutettiin kohdeyrityksessä, toimittajayrityksissä ja asiakasyrityksissä. Haastattelujen tuloksien pohjalta luotiin itsenäinen empiirinen tietotuotekonsepti. Lopullinen tietotuotekonsepti muodostettiin teoreettisen ja empiirisen tietotuotekonseptin pohjalta. Tietotuotekonsepti jaettiin neljään alueeseen: tietotuotteen sisältö ja muoto, tietotuotteen suunnittelu ja toteutus, tietotuotteen yhdistäminen teolliseen internetiin ja tietotuotteen vaikutukset kunnossapitoon. Tietotuotteen sisältö ja muoto jaettiin vielä kolmeen osa-alueeseen: tietosisältö, esitystapa ja käytettävyys. Tärkeimmiksi tuloksiksi voidaan nostaa havainnot, millainen tietotuotteen sisällön ja muodon tulisi olla sekä miten tietotuote tulisi yhdistää teolliseen internetiin

The Data Journalism Handbook

The Data Journalism Handbook: Towards a Critical Data Practice provides a rich and panoramic introduction to data journalism, combining both critical reflection and practical insight. It offers a diverse collection of perspectives on how data journalism is done around the world and the broader consequences of datafication in the news, serving as both a textbook and a sourcebook for this emerging field. With more than 50 chapters from leading researchers and practitioners of data journalism, it explores the work needed to render technologies and data productive for journalistic purposes. It also gives a “behind the scenes” look at the social lives of data sets, data infrastructures, and data stories in newsrooms, media organizations, start-ups, civil society organizations and beyond. The book includes sections on “doing issues with data,” “assembling data,” “working with data,” “experiencing data,” “investigating data, platforms and algorithms,” “organizing data journalism,” “learning data journalism together” and “situating data journalism.

The Data Journalism Handbook

The Data Journalism Handbook: Towards a Critical Data Practice provides a rich and panoramic introduction to data journalism, combining both critical reflection and practical insight. It offers a diverse collection of perspectives on how data journalism is done around the world and the broader consequences of datafication in the news, serving as both a textbook and a sourcebook for this emerging field. With more than 50 chapters from leading researchers and practitioners of data journalism, it explores the work needed to render technologies and data productive for journalistic purposes. It also gives a “behind the scenes” look at the social lives of data sets, data infrastructures, and data stories in newsrooms, media organizations, start-ups, civil society organizations and beyond. The book includes sections on “doing issues with data,” “assembling data,” “working with data,” “experiencing data,” “investigating data, platforms and algorithms,” “organizing data journalism,” “learning data journalism together” and “situating data journalism.