Academic literature on the topic 'Smart data management'
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Journal articles on the topic "Smart data management"
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Gonsalves, Sneha Leleat. "Smart Traffic Management Using Data Analysis." International Journal for Research in Applied Science and Engineering Technology V, no. IX (September 30, 2017): 948–53. http://dx.doi.org/10.22214/ijraset.2017.9139.
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Aberer, Karl, Gustavo Alonso, and Donald Kossmann. "Data management for a smart earth." ACM SIGMOD Record 35, no. 4 (December 2006): 40–45. http://dx.doi.org/10.1145/1228268.1228277.
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Le Dinh, Thang, Nguyen Anh Khoa Dam, Chan Nam Nguyen, Thi My Hang Vu, and Nguyen Cuong Pham. "From Customer Data to Smart Customer Data: The Smart Data Transformation Process." ITM Web of Conferences 41 (2022): 05002. http://dx.doi.org/10.1051/itmconf/20224105002.
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Nowadays, smart data has emerged as a new trend in creating more business value for enterprises that is defined as the data that is gathered and processed to create new insights to support business decisions. However, the transformation from data into actionable insights is still a real challenge for enterprises. For this reason, this paper presents a smart data transformation process, which aims at transforming customer data into smart customer data in order to offer actionable insights. The purpose of the study is to propose a transformation process that can be used to operate a knowledge structure for a smart service system, which can manage and deliver smart data as a service. The process covers the three dimensions of a service system: Data processing corresponding to the engineering dimension, information processing corresponding to the science dimension, and knowledge processing corresponding to the management dimension for knowledge processing. Accordingly, a case study on the smart data transformation process of a customer journey management system as a smart service system is presented to demonstrate the application of the proposed process.
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Sanakkayala SatyaNarayana, Dr, G. V. Sai Bharath, Katakam Sri Lakshmi Sahithi, and Adusumilli Sai Rutwik. "Data Management in IOT Applications." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 224. http://dx.doi.org/10.14419/ijet.v7i2.32.15572.
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With the technology leaping towards a new phase the next big that is happening is IOT and managing the huge amount of data that is being produced. To apprehend the real Internet of Things in which the entirely is interconnected, direct interactions between sensors and actuators, also known as bindings, are essential. As more and more devices are getting connected to the internet there is a lot of data that is being generated. We need to maintain the quality of data and it should be manageable for future use. Consequently, in evaluation to subsisting studies on smart cities we give a information driven edge depicting the central information administration methodologies employed to check consistency, interoperability, granularity and re-convenience of the information created by strategies for the fundamental Internet of Things( IoT) for smart cities. We try to find the proper communication between the devices and finally try to implement the details for a system. In this paper we are trying to do survey on how the large amount of data is being stored and various strategies for handling the data by using some architectures for the smart traffic system. We are trying to use the SWIFT architecture for analyzing the traffic in smart cities.
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Amović, Mladen, Miro Govedarica, Aleksandra Radulović, and Ivana Janković. "Big Data in Smart City: Management Challenges." Applied Sciences 11, no. 10 (May 17, 2021): 4557. http://dx.doi.org/10.3390/app11104557.
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Smart cities use digital technologies such as cloud computing, Internet of Things, or open data in order to overcome limitations of traditional representation and exchange of geospatial data. This concept ensures a significant increase in the use of data to establish new services that contribute to better sustainable development and monitoring of all phenomena that occur in urban areas. The use of the modern geoinformation technologies, such as sensors for collecting different geospatial and related data, requires adequate storage options for further data analysis. In this paper, we suggest the biG dAta sMart cIty maNagEment SyStem (GAMINESS) that is based on the Apache Spark big data framework. The model of the GAMINESS management system is based on the principles of the big data modeling, which differs greatly from standard databases. This approach provides the ability to store and manage huge amounts of structured, semi-structured, and unstructured data in real time. System performance is increasing to a higher level by using the process parallelization explained through the five V principles of the big data paradigm. The existing solutions based on the five V principles are focused only on the data visualization, not the data themselves. Such solutions are often limited by different storage mechanisms and by the ability to perform complex analyses on large amounts of data with expected performance. The GAMINESS management system overcomes these disadvantages by conversion of smart city data to a big data structure without limitations related to data formats or use standards. The suggested model contains two components: a geospatial component and a sensor component that are based on the CityGML and the SensorThings standards. The developed model has the ability to exchange data regardless of the used standard or the data format into proposed Apache Spark data framework schema. The verification of the proposed model is done within the case study for the part of the city of Novi Sad.
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Mocanu, Mariana, Valentin Cristea, Ciprian Dobre, and Florin Pop. "Smart Data for ICT-based Water Management." Forum geografic XV, Suppl. 2 (December 30, 2016): 73–84. http://dx.doi.org/10.5775/fg.2016.096.s.
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Liu, Xiufeng, Alfred Heller, and Per Sieverts Nielsen. "CITIESData: a smart city data management framework." Knowledge and Information Systems 53, no. 3 (April 12, 2017): 699–722. http://dx.doi.org/10.1007/s10115-017-1051-3.
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Chi, Hao, and Yuyan Chi. "Smart Home Control and Management Based on Big Data Analysis." Computational Intelligence and Neuroscience 2022 (February 10, 2022): 1–14. http://dx.doi.org/10.1155/2022/3784756.
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In order to improve the effect of smart home control and management, a new smart home control and management method based on big data analysis is designed. The basic hardware of smart home control and management is designed, including smoke sensor hardware, temperature and humidity sensor hardware, and infrared sensor hardware, so as to collect smart home data and realize data visualization and buzzer alarm. The collected data are transmitted through the indoor wireless network of smart home gateway equipment, and the data distributed cache architecture based on big data analysis is used to store smart home data. Based on the relevant data, the hybrid particle swarm optimization algorithm is used to schedule the control and management tasks of smart home to complete the control and management of smart home. The experimental results show that the device control and scenario management effect of this method is better, and the communication performance is superior and has high practical application value.
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Montalvo, Isis. "How smart are your data?" Nursing Management (Springhouse) 44, no. 6 (June 2013): 23–24. http://dx.doi.org/10.1097/01.numa.0000430412.80830.e6.
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Souifi, Amel, Zohra Cherfi Boulanger, Marc Zolghadri, Maher Barkallah, and Mohamed Haddar. "From Big Data to Smart Data: Application to performance management." IFAC-PapersOnLine 54, no. 1 (2021): 857–62. http://dx.doi.org/10.1016/j.ifacol.2021.08.100.
Full textDissertations / Theses on the topic "Smart data management"
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UGLIOTTI, FRANCESCA MARIA. "BIM and Facility Management for smart data management and visualization." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2696432.
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Il BIM è per tutti gli edifici. Riconosciuta tra le disruptive technologies, la metodologia BIM cambia completamente il modo tradizionale di lavorare dell’industria delle costruzioni, a partire dalla fase di progettazione. In questo scenario, la sfida più interessante è quella di stabilire un framework, che riunisca metodi e strumenti per il ciclo di vita degli edifici, per la gestione del costruito. Il paradigma di Smart city si declina anche nella disponibilità di smart data, includendo, quindi, l’utilizzo intelligente delle informazioni riguardanti il patrimonio immobiliare. Il coinvolgimento proattivo del Facility Management nel processo edilizio è la chiave per garantire la disponibilità di un dataset appropriato di informazioni, supportando l’idea di un sistema di gestione della conoscenza basato sul BIM. In linea con questo approccio, un processo di management impostato a partire dal BIM è conseguibile attraverso una re-ingegnerizzazione complessiva della filiera atta a garantire l’efficacia del BIM ed a fornire servizi intelligenti di Facility 4.0.BIM is for all buildings. As a disruptive technology, BIM completely changes the traditional way of working of the Construction Industry, starting from the design stage. However, the challenging issue is to establish a framework that brings together methods and tools for the buildings lifecycle, focusing on the existing buildings management. Smart city means smart data, including, therefore, intelligent use of Real Estate information. Involving Facility Management in the process is the key to ensure the availability of the proper dataset of information, supporting the idea of a BIM-based knowledge management system. According to this approach, BIM Management is achievable applying a reverse engineering process to guarantee the BIM effectiveness and to provide Facility 4.0 smart services.
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Fares, Tony Yussef. "Digital rights management for smart containment objects." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060511.151012/index.html.
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Moreira, Helder. "Sensor data integration and management of smart environments." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/17884.
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Mestrado em Engenharia de Computadores e TelemáticaNum mundo de constante desenvolvimento tecnológico e acelerado crescimento populacional, observa-se um aumento da utilização de recursos energéticos. Sendo os edifícios responsáveis por uma grande parte deste consumo energético, desencadeiam-se vários esforços de investigações de forma a criarem-se edifícios energeticamente eficientes e espaços inteligentes. Esta dissertação visa, numa primeira fase, apresentar uma revisão das atuais soluções que combinam sistemas de automação de edifícios e a Internet das Coisas. Posteriormente, é apresentada uma solução de automação para edifícios, com base em princípios da Internet das Coisas e explorando as vantagens de sistemas de processamento complexo de eventos, de forma a fornecer uma maior integração dos múltiplos sistemas existentes num edifício. Esta solução é depois validada através de uma implementação, baseada em protocolos leves desenhados para a Internet das Coisas, plataformas de alto desempenho, e métodos complexos para análise de grandes fluxos de dados. Esta implementação é ainda aplicada num cenário real, e será usada como a solução padrão para gestão e automação num edifício existente.
In a world of constant technological development and accelerated population growth, an increased use of energy resources is being observed. With buildings responsible for a large share of this energy consumption, a lot of research activities are pursued with the goal to create energy efficient buildings and smart spaces. This dissertation aims to, in a first stage, present a review of the current solutions combining Building Automation Systems (BAS) and Internet of Things (IoT). Then, a solution for building automation is presented based on IoT principles and exploiting the advantages of Complex Event Processing (CEP) systems, to provide higher integration of the multiple building subsystems. This solution was validated through an implementation, based on standard lightweight protocols designed for IoT, high performance and real time platforms, and complex methods for analysis of large streams of data. The implementation is also applied to a real world scenario, and will be used as a standard solution for management and automation of an existing building
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DEL, GIUDICE MATTEO. "Smart data management with BIM for Architectural Heritage." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2652020.
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In the last years smart buildings topic has received much attention as well as Building Information Modelling (BIM) and interoperability as independent fields.
Linking these topics is an essential research target to help designers and stakeholders to run processes more efficiently. Working on a smart building requires the use of Innovation and Communication Technology (ICT) to optimize design, construction and management. In these terms, several technologies such as sensors for remote monitoring and control, building equipment, management software, etc. are available in the market.
As BIM provides an enormous amount of information in its database and theoretically it is able to work with all kind of data sources using interoperability, it is essential to define standards for both data contents and format exchange. In this way, a possibility to align research activity with Horizon 2020 is the investigation of energy saving using ICT. Unfortunately, comparing the Architecture Engineering and Construction (AEC) Industry with other sectors it is clear how in the building field advanced information technology applications have not been adopted yet. However in the last years, the adoption of new methods for the data management has been investigated by many researchers.
So, basing on the above considerations, the main purpose of this thesis is investigate the use of BIM methodology relating to existing buildings concerning on three main topics:
• Smart data management for architectural heritage preservation;
• District data management for energy reduction;
• The maintenance of highrises.
For these reasons, data management acquires a very important value relating to the optimization of the building process and it is considered the most important goal for this research. Taking into account different kinds of architectural heritage, the attention is focused on the existing and historical buildings that usually have characterized by several constraints.
Starting from data collection, a BIM model was developed and customized in function of its objectives, and providing information for different simulation tests. Finally, data visualization was investigated through the Virtual Reality(VR) and Augmented Reality (AR). Certainly, the creation of a 3D parametric model implies that data is organized according to the use of individual users that are involved in the building process. This means that each 3D model can be developed with different Levels of Detail/Development (LODs) basing on the goal of the data source.
Along this thesis the importance of LODs is taken into account related to the kind of information filled in a BIM model. In fact, basing on the objectives of each project a BIM model can be developed in a different way to facilitate the querying data for the simulations tests.
The three topics were compared considering each step of the building process workflow, highlighting the main differences, evaluating the strengths and weaknesses of BIM methodology. In these terms, the importance to set a BIM template before the modelling step was pointed out, because it provides the possibility to manage information in order to be collected and extracted for different purposes and by specific users. Moreover, basing on the results obtained in terms of the 3D parametric model and in terms of process, a proper BIM maturity level was determined for each topic.
Finally, the value of interoperability was arisen from these tests considering that it provided the opportunity to develop a framework for collaboration, involving all parties of the building industry.
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Simonet, Anthony. "Active Data - Enabling Smart Data Life Cycle Management for Large Distributed Scientific Data Sets." Thesis, Lyon, École normale supérieure, 2015. http://www.theses.fr/2015ENSL1004/document.
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Dans tous les domaines, le progrès scientifique repose de plus en plus sur la capacité à exploiter des volumes de données toujours plus gigantesques. Alors que leur volume augmente, la gestion de ces données se complexifie. Un point clé est la gestion du cycle de vie des données, c'est à dire les diverses opérations qu'elles subissent entre leur création et leur disparition : transfert, archivage, réplication, suppression, etc. Ces opérations, autrefois simples, deviennent ingérables lorsque le volume des données augmente de manière importante, au vu de l'hétérogénéité des logiciels utilisés d'une part, et de la complexité des infrastructures mises en œuvre d'autre part.Nous présentons Active Data, un méta-modèle, une implémentation et un modèle de programmation qui permet de représenter formellement et graphiquement le cycle de vie de données présentes dans un assemblage de systèmes et d'infrastructures hétérogènes, en exposant naturellement la réplication, la distribution et les différents identifiants des données. Une fois connecté à des applications existantes, Active Data expose aux utilisateurs ou à des programmes l'état d'avancement des données dans leur cycle de vie, en cours d'exécution, tout en gardant leur trace lorsqu'elles passent d'un système à un autre.Le modèle de programmation Active Data permet d'exécuter du code à chaque étape du cycle de vie des données. Les programmes écrits avec Active Data ont à tout moment accès à l'état complet des données, à la fois dans tous les systèmes et dans toutes les infrastructures sur lesquels elles sont distribuées. Nous présentons des évaluations de performance et des exemples d'utilisation qui attestent de l'expressivité du modèle de programmation et de la qualité de l'implémentation. Enfin, nous décrivons l'implémentation d'un outil de Surveillance des données basé sur Active Data pour l'expérience Advanced Photon Source qui permet aux utilisateurs de suivre la progression de leurs données, d'automatiser la plupart des tâches manuelles, d'obtenir des notifications pertinente parmi une masse gigantesque d'événements, ainsi que de détecter et corriger de nombreuses erreurs sans intervention humaine.Ce travail propose des perspectives intéressantes, en particulier dans les domaines de la provenance des données et de l'open data, tout en facilitant la collaboration entre les scientifiques de communautés différentesIn all domains, scientific progress relies more and more on our ability to exploit ever growing volumes of data. However, as datavolumes increase, their management becomes more difficult. A key point is to deal with the complexity of data life cycle management,i.e. all the operations that happen to data between their creation and there deletion: transfer, archiving, replication, disposal etc.These formerly straightforward operations become intractable when data volume grows dramatically, because of the heterogeneity ofdata management software on the one hand, and the complexity of the infrastructures involved on the other.In this thesis, we introduce Active Data, a meta-model, an implementation and a programming model that allow to represent formally and graphically the life cycle of data distributed in an assemblage of heterogeneous systems and infrastructures, naturally exposing replication, distribution and different data identifiers. Once connected to existing applications, Active Data exposes the progress of data through their life cycle at runtime to users and programs, while keeping their track as it passes from a system to another.The Active Data programming model allows to execute code at each step of the data life cycle. Programs developed with Active Datahave access at any time to the complete state of data in any system and infrastructure it is distributed to.We present micro-benchmarks and usage scenarios that demonstrate the expressivity of the programming model and the implementationquality. Finally, we describe the implementation of a Data Surveillance framework based on Active Data for theAdvanced Photon Source experiment that allows scientists to monitor the progress of their data, automate most manual tasks,get relevant notifications from huge amount of events, and detect and recover from errors without human intervention.This work provides interesting perspectives in data provenance and open data in particular, while facilitating collaboration betweenscientists from different communities
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Christiansen, Filip, and Matilda Tranell. "Data Management and Business Opportunities inEmerging Smart Metering Market." Thesis, KTH, Energiteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-206975.
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Major changes in the energy systems throughout Europe have resulted in the implementation of new technologies such as smart grids and meters, enabling a two-way flow of information and electricity. This results in large volumes of metering data which needs to be efficiently managed for market and grid operational purposes. In addition to this, a new market for third parties seeking to enhance and convert data into valuable information has emerged. Current data management strategies vary between countries, resulting in a great diversity of data management models. To reach consensus, the European Commission has developed three theoretical reference models in order to cover all possible options. For the success of third parties, it is important to understand the rather complex mechanisms of these reference models. This can ease the process of recognizing the implemented data management model on a given market, as well as the interaction with related obstacles or barriers, in order to determine business opportunities. This report aims to present market conditions for third party actors in two European countries that have implemented different data management models. The Netherlands and Great Britain are selected based on certain conditions. With existing theories of the reference models, the actual models will be defined in each country. Key barriers are also identified. This report will then study how appropriate the implemented models are in relation to the barriers. Therefore, these two countries will also serve as case studies for evaluating the applicability of the reference models. In the Netherlands, case 1 of the reference models is identified per definition, although a transitioning towards case 2 can be observed. The major barrier consists of privacy concerns although customer engagement is becoming a central focus. In relation to these issues, targeted regulations seems to have more positive impact than the implemented model. The Dutch market is evolving and it is shown that the customers are open to new innovative services, although the intent to purchase such services is low. A central point of access to data facilitates efficient data management, however this only includes data with a 15 minute frequency. Data with a 10 second update interval can currently be accessed only via a physical smart meter port. In Great Britain, parts from both reference model 2 and 3 are implemented and the main barrier is currently customer engagement. The model has been developed with high emphasis on earlier privacy concerns, but it has potential to also address customer engagement by supporting innovation and new services. However, earlier restrictive regulations have only allowed certain feedback services, i.e. In Home Displays, to be offered to customers. As of 2015, other options are allowed which opens up a promising market for third party actors. Data can be accessed either centrally, with half-hourly updates, or via so called Consumer Access Devices providing data with updates every 10 second. A gap between the theoretical models and reality is observed; theoretical benefits are not always evident in practice. It is also observed how all possible data flows are not always properly described or included in data management model mappings. Therefore, it is important for third parties to look beyond such mappings to understand the access to certain data that fits their purpose. At last, privacy concerns can be eased through increased customer awareness and empowerment, which is also related to the receptivity to innovations among customers.Uppkomsten av smarta elnät och elmätare möjliggör ett dubbelriktat flöde av information i elnät. Detta ger upphov till stora datamängder och för marknadsaktiviteter och elnätsrelaterade åtaganden krävs därför en effektiv datahantering. Dessutom uppstår en ny marknad för tredjepartsaktörer som kan använda datan och göra om den till värdefull information. Strategier för hur datahanteringen ska gå till skiljer sig åt mellan länder och mångfalden är stor. Europeiska Kommissionen har tagit fram tre olika teoretiska referensmodeller för att uppnå konsensus inom detta område. Dessa modeller kan fungera som verktyg för tredjepartsaktörer i syfte att identifiera verkliga modeller för datahantering. Dessutom kan de ge värdefull information om relationen mellan datahantering och försvårande omständigheter; något som är viktigt att förstå för att bedöma marknadsmöjligheter. Målet med denna rapport är att presentera marknadsmöjligheter för tredjepartsaktörer i två europeiska länder som har olika modeller för datahantering. Utifrån särskilda kriterier väljs Nederländerna och England. Med hjälp av existerande teori kring referensmodellerna definieras de reella modellerna i länderna. Därefter utreder rapporten hur lämpliga de reella modellerna är i relation till identifierade barriärer. Därmed fungerar de två länderna även som fallstudier för utvärdering av applicerbarheten hos referensmodellerna. I Nederländerna identifieras den verkliga modellen för datahantering som en variant av modell 1 av referensmodellerna, och en utveckling mot modell 2 kan observeras. Den avgörande barriären är integritetsrelaterad, men kundengagemang blir ett alltmer centralt fokus. I relation till dessa problem kan det konstateras att specifika regleringar har större positiv genomslagskraft än själva modellen. Den holländska marknaden befinner sig i ett tidigt utvecklingsstadie men det har visat sig att kunder är positivt inställda till innovativa tjänster. Effektiv datahantering främjas av en central åtkomstpunkt, men detta inkluderar endast data med en uppdateringsfrekvens om 15 minuter. Data med uppdateringsfrekvens om 10 sekunder är tillgänglig via en fysisk port på själva elmätaren. I England identifieras den verkliga modellen för datahantering som delar av både referensmodell 2 och 3, och den största barriären är brist på kundengagemang. Tidigare utbredda integritetsproblem har delvis utformat modellen, men trots detta återfinns positiva funktioner sett till rådande utmaning då modellen främjar högre innovationsnivåer för tjänster. Regleringar har dock tidigare begränsat utbudet av sådana tjänster till endast s.k In Home Displays. Under 2015 förändrades denna reglering vilket medför lovande marknadsmöjligheter för tredjepartsaktörer. Datatillgång sker antingen via en central åtkomstpunkt, med en uppdateringsfrekvensen om 30 minuter, eller via s.k Consumer Access Devices där uppdateringsfrekvensen är 10 sekunder. Ett gap mellan de teoretiska modellerna och den verkliga implementeringen kan observeras eftersom teoretiskt beskrivna fördelar inte alltid förekommer i praktiken. En annan viktig upptäckt är att visualiseringar av datamodeller inte alltid beskriver eller inkluderar samtliga dataflöden. Därmed bör tredjepartsaktörer inte enbart förlita sig på sådana kartläggningar; andra metoder kan vara nödvändiga för att bedöma tillgången till nödvändig data. Till sist kan det konstateras att integritetsproblem kan motverkas med metoder som ökar uppmärksamheten hos kunder. Ett viktigt samband mellan detta och mottagligheten för innovation hos kunderna kan påvisas.
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Masilela, Mbonisi. "Supporting Data-Intensive Wireless Sensor Applications using Smart Data Fragmentation and Buffer Management." VCU Scholars Compass, 2007. http://scholarscompass.vcu.edu/etd/779.
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Recent advances in low power device technology have led to the development of smaller powerful sensors geared for use in Wireless Sensor Networks. Some of these sensors are capable of producing large data packets in a single reading. This becomes a challenging problem given the constraints imposed by current MAC and Transport Layer implementations since a single data packet can exceed the MTU of the protocol stack. Little has been done in the way of addressing this issue in Wireless Sensor Networks. This paper proposes a novel solution to this issue. Proposed is a Lightweight Data Transportation Protocol that uses smart data fragmentation and efficient pipelined transmission and buffer management schemes to solve this problem. The methodology outlined in this paper ensures that data is successfully transmitted from source to destination with minimal delay or packet loss.
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Sinaeepourfard, Amir. "Hierarchical distributed fog-to-cloud data management in smart cities." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/461740.
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There is a vast amount of data being generated every day in the world with different formats, quality levels, etc. This new data, together with the archived historical data, constitute the seed for future knowledge discovery and value generation in several fields of science and big data environments. Discovering value from data is a complex computing process where data is the key resource, not only during its processing, but also during its entire life cycle. However, there is still a huge concern about how to organize and manage this data in all fields for efficient usage and exploitation during all data life cycles. Although several specific Data LifeCycle (DLC) models have been recently defined for particular scenarios, we argue that there is no global and comprehensive DLC framework to be widely used in different fields.
In particular scenario, smart cities are the current technological solutions to handle the challenges and complexity of the growing urban density. Traditionally, Smart City resources management rely on cloud based solutions where sensors data are collected to provide a centralized and rich set of open data. The advantages of cloud-based frameworks are their ubiquity, as well as an (almost) unlimited resources capacity. However, accessing data from the cloud implies large network traffic, high latencies usually not appropriate for real-time or critical solutions, as well as higher security risks. Alternatively, fog computing emerges as a promising technology to absorb these inconveniences. It proposes the use of devices at the edge to provide closer computing facilities and, therefore, reducing network traffic, reducing latencies drastically while improving security. We have defined a new framework for data management in the context of a Smart City through a global fog to cloud resources management architecture. This model has the advantages of both, fog and cloud technologies, as it allows reduced latencies for critical applications while being able to use the high computing capabilities of cloud technology.
In this thesis, we propose many novel ideas in the design of a novel F2C Data Management architecture for smart cities as following. First, we draw and describe a comprehensive scenario agnostic Data LifeCycle model successfully addressing all challenges included in the 6Vs not tailored to any specific environment, but easy to be adapted to fit the requirements of any particular field. Then, we introduce the Smart City Comprehensive Data LifeCycle model, a data management architecture generated from a comprehensive scenario agnostic model, tailored for the particular scenario of Smart Cities. We define the management of each data life phase, and explain its implementation on a Smart City with Fog-to-Cloud (F2C) resources management. And then, we illustrate a novel architecture for data management in the context of a Smart City through a global fog to cloud resources management architecture. We show this model has the advantages of both, fog and cloud, as it allows reduced latencies for critical applications while being able to use the high computing capabilities of cloud technology. As a first experiment for the F2C data management architecture, a real Smart City is analyzed, corresponding to the city of Barcelona, with special emphasis on the layers responsible for collecting the data generated by the deployed sensors. The amount of daily sensors data transmitted through the network has been estimated and a rough projection has been made assuming an exhaustive deployment that fully covers all city. And, we provide some solutions to both reduce the data transmission and improve the data management. Then, we used some data filtering techniques (including data aggregation and data compression) to estimate the network traffic in this model during data collection and compare it with a traditional real system. Indeed, we estimate the total data storage sizes through F2C scenario for Barcelona smart citiesAl món es generen diàriament una gran quantitat de dades, amb diferents formats, nivells de qualitat, etc. Aquestes noves dades, juntament amb les dades històriques arxivades, constitueixen la llavor per al descobriment de coneixement i la generació de valor en diversos camps de la ciència i grans entorns de dades (big data). Descobrir el valor de les dades és un procés complex de càlcul on les dades són el recurs clau, no només durant el seu processament, sinó també durant tot el seu cicle de vida. Tanmateix, encara hi ha una gran preocupació per com organitzar i gestionar aquestes dades en tots els camps per a un ús i explotació eficients durant tots els cicles de vida de les dades. Encara que recentment s'han definit diversos models específics de Data LifeCycle (DLC) per a escenaris particulars, argumentem que no hi ha un marc global i complet de DLC que s'utilitzi àmpliament en diferents camps. En particular, les ciutats intel·ligents són les solucions tecnològiques actuals per fer front als reptes i la complexitat de la creixent densitat urbana. Tradicionalment, la gestió de recursos de Smart City es basa en solucions basades en núvol (cloud computing) on es recopilen dades de sensors per proporcionar un conjunt de dades obert i centralitzat. Les avantatges dels entorns basats en núvol són la seva ubiqüitat, així com una capacitat (gairebé) il·limitada de recursos. Tanmateix, l'accés a dades del núvol implica un gran trànsit de xarxa i, en general, les latències elevades no són apropiades per a solucions crítiques o en temps real, així com també per a riscos de seguretat més elevats. Alternativament, el processament de boira (fog computing) sorgeix com una tecnologia prometedora per absorbir aquests inconvenients. Proposa l'ús de dispositius a la vora per proporcionar recuirsos informàtics més propers i, per tant, reduir el trànsit de la xarxa, reduint les latències dràsticament mentre es millora la seguretat. Hem definit un nou marc per a la gestió de dades en el context d'una ciutat intel·ligent a través d'una arquitectura de gestió de recursos des de la boira fins al núvol (Fog-to-Cloud computing, o F2C). Aquest model té els avantatges combinats de les tecnologies de boira i de núvol, ja que permet reduir les latències per a aplicacions crítiques mentre es poden utilitzar les grans capacitats informàtiques de la tecnologia en núvol. En aquesta tesi, proposem algunes idees noves en el disseny d'una arquitectura F2C de gestió de dades per a ciutats intel·ligents. En primer lloc, dibuixem i descrivim un model de Data LifeCycle global agnòstic que aborda amb èxit tots els reptes inclosos en els 6V i no adaptats a un entorn específic, però fàcil d'adaptar-se als requisits de qualsevol camp en concret. A continuació, presentem el model de Data LifeCycle complet per a una ciutat intel·ligent, una arquitectura de gestió de dades generada a partir d'un model agnòstic d'escenari global, adaptat a l'escenari particular de ciutat intel·ligent. Definim la gestió de cada fase de la vida de les dades i expliquem la seva implementació en una ciutat intel·ligent amb gestió de recursos F2C. I, a continuació, il·lustrem la nova arquitectura per a la gestió de dades en el context d'una Smart City a través d'una arquitectura de gestió de recursos F2C. Mostrem que aquest model té els avantatges d'ambdues, la tecnologia de boira i de núvol, ja que permet reduir les latències per a aplicacions crítiques mentre es pot utilitzar la gran capacitat de processament de la tecnologia en núvol. Com a primer experiment per a l'arquitectura de gestió de dades F2C, s'analitza una ciutat intel·ligent real, corresponent a la ciutat de Barcelona, amb especial èmfasi en les capes responsables de recollir les dades generades pels sensors desplegats. S'ha estimat la quantitat de dades de sensors diàries que es transmet a través de la xarxa i s'ha realitzat una projecció aproximada assumint un desplegament exhaustiu que cobreix tota la ciutat.
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Finotto, Gianluca <1988>. "Smart Data: un nuovo asset intangibile a supporto del management." Master's Degree Thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/8802.
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L’affinamento delle tecnologie ha portato ad una crescita esponenziale dei devices in grado di automatizzare numerose operazioni, sia in ambito produttivo che nella vita privata. Inoltre, l’evoluzione economica dei paesi in via di sviluppo ha permesso una rapida e capillare diffusione di internet in tutto il mondo.
I Big Data hanno origine quindi dalla quantità di dati digitali attualmente a disposizione, generati sempre più in maniera automatica e veloce dagli individui nel contesto privato, nell’ambiente fisico oppure in azienda (attraverso smartphone, card magnetiche, sensori, GPS, etc.), da cose (auto, beni in movimento, etc.) e dagli eventi (meteo, atterraggio degli aerei, pagamento finanziario, malfunzionamento di un distributore, etc.).
Raggruppare ed analizzare adeguatamente questi zettabyte di dati strutturati e non, è fondamentale per supportare l'attività di decision-making. Tuttavia, non sempre il volume è direttamente proporzionale alla qualità: affinchè i Big Data non siano solo fini a sé stessi ma diventino “smart”, ovvero possano produrre un valore aggiunto per le organizzazioni, è necessaria una diffusione all’interno delle organizzazioni sia pubbliche che private di un’impostazione data driven, cioè un’effettiva cultura legata al data management, in modo da orientare gli investimenti futuri in adeguare infrastrutture e conoscenze tecniche e manageriali.
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Stivanello, Alice <1993>. "Strategic Management over Data Privacy and Cyber Security Risk in Smart City and Smart Home." Master's Degree Thesis, Università Ca' Foscari Venezia, 2018. http://hdl.handle.net/10579/12673.
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The world population growth combined with the unprecedented levels of urban density is posing serious challenges for the future of our cities which demand an efficient, effective, and sustainable management of urban infrastructures and resource consumption. Through the integration of information and communication technologies (ICT), the smart city is identified as a ‘system-of-systems’ created to process real-time information exchange at a large-scale and consequently distribute a better life quality to its citizens. Grounded in learning capability and cross-domain interoperability, the embedded Internet of Things (IoT) infrastructure represents a high-value attack platform and thus its adoption should be carefully weighed up against the cyber risk exposure. The main objective of this research is to explore the inner workings of a such complex ecosystem and understand the criticalities of the cyber-security requirements.
Since the smart home market represents a fundamental component of a smart city and the most promising application of IoT technology, an accurate investigation is carried out. Defining the smart home as an intertwined advanced automated system which provide the inhabitants remote access and centralized control over the building’s functions, the role played by the advancement of IoT technology is crucial. A multi-layer architectural model is presented in order to grasp the logical conditions underlying the intelligence-driven networks. Installed under the guise of customer service, surveillance facility and remote monitoring are responsible for the potential abuse of data retrieved and thus the failure of safety and security solutions. In response, a cyber-physical vulnerability assessment is conducted and evaluated into a threat-based Defence approach.
The scope of this thesis is the identification and formulation of a safe and secure human-machine space, associating proper countermeasures to prevent data leakages and mitigate damages. Although this analysis tries to be exhaustive in all its part, the major focus is on cyber-security concern as it represents a significant barrier to smart systems adoption and all stakeholders should take it seriously. Neglecting the current cyber-security vulnerabilities and underestimate the impact of a cyber intrusion may reveal cascading disasters across the entire smart industry.
Books on the topic "Smart data management"
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George, James A. Smart data: Enterprise performance optimization strategy. Hoboken, N.J: John Wiley & Sons, 2010.
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George, James A. Smart data: Enterprise performance optimization strategy. Hoboken, N.J: John Wiley & Sons, 2010.
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1952-, Rodger James A., ed. Smart data: Enterprise performance optimization strategy. Hoboken, N.J: John Wiley & Sons, 2010.
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George, James A. Smart data: Enterprise performance optimization strategy. Hoboken, N.J: John Wiley & Sons, 2010.
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George, James A. Smart data: Enterprise performance optimization strategy. Hoboken, N.J: John Wiley & Sons, 2010.
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Network management with smart systems. New York: McGraw-Hill, 1994.
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Christine, Hertzog, ed. Data privacy for the smart grid. Boca Raton: CRC Press, Taylor & Francis Group, 2015.
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Jiang, Tao, Liang Yu, and Yang Cao. Energy Management of Internet Data Centers in Smart Grid. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45676-7.
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Big data analytics strategies for the smart grid. Boca Raton: CRC Press, 2015.
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Ur Rehman, Masood, and Ahmed Zoha, eds. Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95593-9.
Full textBook chapters on the topic "Smart data management"
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Benning, Justus, and Lucas Wenger. "Smart-Data-Management." In Digitalisierungs- und Informationsmanagement, 175–211. Berlin, Heidelberg: Springer Berlin Heidelberg, 2022. http://dx.doi.org/10.1007/978-3-662-63758-6_9.
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Budka, Kenneth C., Jayant G. Deshpande, and Marina Thottan. "Smart Grid Data Management." In Communication Networks for Smart Grids, 265–84. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-6302-2_10.
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Chen, Du, and Xindong Ni. "Smart Farming Management." In Sensing, Data Managing, and Control Technologies for Agricultural Systems, 185–202. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-03834-1_8.
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Dearle, Alan, Graham Kirby, Ron Morrison, Andrew McCarthy, Kevin Mullen, Yanyan Yang, Richard Connor, Paula Welen, and Andy Wilson. "Architectural Support for Global Smart Spaces." In Mobile Data Management, 153–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36389-0_11.
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Strang, Thomas. "Towards Autonomous Services for Smart Mobile Devices." In Mobile Data Management, 279–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36389-0_19.
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Gupta, Rekha, Neha Budhiraja, Shreya Mago, and Shivani Mathur. "An IoT-Based Smart Parking Framework for Smart Cities." In Data Management, Analytics and Innovation, 19–32. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5616-6_2.
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Ramamritham, Krithi, Gopinath Karmakar, and Prashant Shenoy. "Smart Energy Management: A Computational Approach." In Big Data Analytics, 3–14. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-72413-3_1.
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Baier, Melanie. "Data Science im HR-Management." In Smart Human Resource Management, 115–32. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-30267-2_8.
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Thasnimol, C. M., and R. Rajathy. "Synchrophasor data management: A high-performance computing perspective." In Smart Computing, 354–67. London: CRC Press, 2021. http://dx.doi.org/10.1201/9781003167488-41.
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Abdeljebbar, Najat, Laila Moussaid, and Hicham Medromi. "Smart Water Management: Pillars and Technologies." In Studies in Big Data, 7–14. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12048-1_2.
Full textConference papers on the topic "Smart data management"
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"Smart Learning Management System Framework." In International Conference on Data Technologies and Applications. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0004083102290234.
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Burdeniuk, Adam, Kiet To, and Cheng Chew Lim. "A data management layer for parallel matrix computation." In Smart Materials, Nano- and Micro-Smart Systems, edited by Said F. Al-Sarawi. SPIE, 2006. http://dx.doi.org/10.1117/12.695626.
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Kettouch, Mohamed, Cristina Luca, Ouissem Khorief, Rui Wu, and Sergiu Dascalu. "Semantic data management in Smart Cities." In 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP). IEEE, 2017. http://dx.doi.org/10.1109/optim.2017.7975123.
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EL Hachimi, Chouaib, Salwa Belaqziz, Saïd Khabba, and Abdelghani Chehbouni. "Towards Smart Big Weather Data Management." In IOCAG 2022. Basel Switzerland: MDPI, 2022. http://dx.doi.org/10.3390/iocag2022-12240.
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Fuhrmann, Patrick, Marica Antonacci, Giacinto DONVITO, Oliver Keeble, and Paul MILLAR. "Smart Policy Driven Data Management and Data Federations." In International Symposium on Grids and Clouds 2018 in conjunction with Frontiers in Computational Drug Discovery. Trieste, Italy: Sissa Medialab, 2018. http://dx.doi.org/10.22323/1.327.0001.
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Friedl, Sabrina, Sebastian Schmoll, Felix Borutta, and Matthias Schubert. "SMART-Env." In 2020 21st IEEE International Conference on Mobile Data Management (MDM). IEEE, 2020. http://dx.doi.org/10.1109/mdm48529.2020.00050.
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"Big Data Management and Analytics for Supporting Smart Healthcare Applications." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020839.
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Prithivirajan, Maruthi, and Kyong Jin Shim. "An IoT-Driven Smart Cafe Solution for Human Traffic Management." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005489.
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Bharat, Vinod, Shingare Shubham, Dafade Jagdish, Patil Amol, and Khatke Renuka. "Smart water management system in cities." In 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC). IEEE, 2017. http://dx.doi.org/10.1109/icbdaci.2017.8070846.
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El Khaouat, Atimad, and Laila Benhlima. "Big data based management for smart grids." In 2016 International Renewable and Sustainable Energy Conference (IRSEC). IEEE, 2016. http://dx.doi.org/10.1109/irsec.2016.7983902.
Full textReports on the topic "Smart data management"
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Page, Janie, Chuck McParland, Mary Ann Piette, and Stephen Czarnecki. Design of an Open Smart Energy Gateway for Smart Meter Data Management. Office of Scientific and Technical Information (OSTI), March 2015. http://dx.doi.org/10.2172/1248928.
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Balyk, Nadiia, Svitlana Leshchuk, and Dariia Yatsenyak. Developing a Mini Smart House model. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3741.
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The work is devoted to designing a smart home educational model. The authors analyzed the literature in the field of the Internet of Things and identified the basic requirements for the training model. It contains the following levels: command, communication, management. The authors identify the main subsystems of the training model: communication, signaling, control of lighting, temperature, filling of the garbage container, monitoring of sensor data. The proposed smart home educational model takes into account the economic indicators of resource utilization, which gives the opportunity to save on payment for their consumption. The hardware components for the implementation of the Mini Smart House were selected in the article. It uses a variety of technologies to conveniently manage it and use renewable energy to power it. The model was produced independently by students involved in the STEM project. Research includes sketching, making construction parts, sensor assembly and Arduino boards, programming in the Arduino IDE environment, testing the functioning of the system. Research includes sketching, making some parts, assembly sensor and Arduino boards, programming in the Arduino IDE environment, testing the functioning of the system. Approbation Mini Smart House researches were conducted within activity the STEM-center of Physics and Mathematics Faculty of Ternopil Volodymyr Hnatiuk National Pedagogical University, in particular during the educational process and during numerous trainings and seminars for pupils and teachers of computer science.
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Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3178.
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The authors of the given article continue the series presented by the 2018 paper “Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot”. This time, they consider mathematical informatics as the basis of higher engineering education fundamentalization. Mathematical informatics deals with smart simulation, information security, long-term data storage and big data management, artificial intelligence systems, etc. The authors suggest studying basic principles of mathematical informatics by applying cloud-oriented means of various levels including those traditionally considered supplementary – spreadsheets. The article considers ways of building neural network models in cloud-oriented spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s-1930s is discussed as well as some peculiarities of data selection. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization.
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Leavy, Michelle B., Costas Boussios, Robert L. Phillips, Jr., Diana Clarke, Barry Sarvet, Aziz Boxwala, and Richard Gliklich. Outcome Measure Harmonization and Data Infrastructure for Patient-Centered Outcomes Research in Depression: Final Report. Agency for Healthcare Research and Quality (AHRQ), June 2022. http://dx.doi.org/10.23970/ahrqepcwhitepaperdepressionfinal.
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Objective. The objective of this project was to demonstrate the feasibility and value of collecting harmonized depression outcome measures in the patient registry and health system settings, displaying the outcome measures to clinicians to support individual patient care and population health management, and using the resulting measures data to support patient-centered outcomes research (PCOR). Methods. The harmonized depression outcome measures selected for this project were response, remission, recurrence, suicide ideation and behavior, adverse effects of treatment, and death from suicide. The measures were calculated in the PRIME Registry, sponsored by the American Board of Family Medicine, and PsychPRO, sponsored by the American Psychiatric Association, and displayed on the registry dashboards for the participating pilot sites. At the conclusion of the data collection period (March 2020-March 2021), registry data were analyzed to describe implementation of measurement-based care and outcomes in the primary care and behavioral health care settings. To calculate and display the measures in the health system setting, a Substitutable Medical Apps, Reusable Technology (SMART) on Fast Healthcare Interoperability Resource (FHIR) application was developed and deployed at Baystate Health. Finally a stakeholder panel was convened to develop a prioritized research agenda for PCOR in depression and to provide feedback on the development of a data use and governance toolkit. Results. Calculation of the harmonized outcome measures within the PRIME Registry and PsychPRO was feasible, but technical and operational barriers needed to be overcome to ensure that relevant data were available and that the measures were meaningful to clinicians. Analysis of the registry data demonstrated that the harmonized outcome measures can be used to support PCOR across care settings and data sources. In the health system setting, this project demonstrated that it is technically and operationally feasible to use an open-source app to calculate and display the outcome measures in the clinician’s workflow. Finally, this project produced tools and resources to support future implementations of harmonized measures and use of the resulting data for research, including a prioritized research agenda and data use and governance toolkit. Conclusion. Standardization of outcome measures across patient registries and routine clinical care is an important step toward creating robust, national-level data infrastructure that could serve as the foundation for learning health systems, quality improvement initiatives, and research. This project demonstrated that it is feasible to calculate the harmonized outcome measures for depression in two patient registries and a health system setting, display the results to clinicians to support individual patient management and population health, and use the outcome measures data to support research. This project also assessed the value and burden of capturing the measures in different care settings and created standards-based tools and other resources to support future implementations of harmonized outcome measures in depression and other clinical areas. The findings and lessons learned from this project should serve as a roadmap to guide future implementations of harmonized outcome measures in depression and other clinical areas.
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Thoma, David. Landscape phenology, vegetation condition, and relations with climate at Capitol Reef National Park, 2000–2019. Edited by Alice Wondrak Biel. National Park Service, March 2023. http://dx.doi.org/10.36967/2297289.
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Quantitatively linking satellite observations of vegetation condition and climate data over time provides insight to climate influences on primary production, phenology (timing of growth), and sensitivity of vegetation to weather and longer-term patterns of weather referred to as climate. This in turn provides a basis for understanding potential climate impacts to vegetation—and the potential to anticipate cascading ecological effects, such as impacts to forage, habitat, fire potential, and erosion, as climate changes in the future. This report provides baseline information about vegetation production and condition over time at Capitol Reef National Park (NP), as derived from satellite remote sensing. Its objective is to demonstrate methods of analysis, share findings, and document historic climate exposure and sensitivity of vegetation to weather and climate as a driver of vegetation change. This report represents a quantitative foundation of vegetation–climate relationships on an annual timestep. The methods can be modified to finer temporal resolution and other spatial scales if further analyses are needed to inform park planning and management. The knowledge provided in this report can inform vulnerability assessments for Climate Smart Conservation planning by park managers. Patterns of pivot points and responses can serve as a guide to anticipate what, where, when, and why vegetation change may occur. For this analysis, vegetation alliance groups were derived from vegetation-map polygons (Von Loh et al. 2007) by lumping vegetation types expected to respond similarly to climate. Relationships between vegetation production and phenology were evaluated for each alliance map unit larger than a satellite pixel (~300 × 300 m). We used a water-balance model to characterize the climate experienced by plants. Water balance translates temperature and precipitation into more biophysically relevant climate metrics, such as soil moisture and drought stress, that are often more strongly correlated with vegetation condition than temperature or precipitation are. By accounting for the interactions between temperature, precipitation, and site characteristics, water balance helps make regional climate assessments relevant to local scales. The results provide a foundation for interpreting weather and climate as a driver of changes in primary production over a 20-year period at the polygon and alliance-group scale. Additionally, they demonstrate how vegetation type and site characteristics, such as soil properties, slope, and aspect, interact with climate at local scales to determine trends in vegetation condition. This report quantitatively defines critical water needs of vegetation and identifies which alliance types, in which locations, may be most susceptible to climate-change impacts in the future. Finally, this report explains how findings can be used in the Climate Smart Conservation framework, with scenario planning, to help manage park resources through transitions imposed by climate change.