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A service-oriented architecture for aggregating ontological information from distributed data nodes for internet of things.Semantic technologies serve as the solution to the heterogeneity of applications.Product tracing is taken as example to demonstrate the work flow of the proposed architecture. Display Omitted The current Internet of Things (IoT) has made it very convenient to obtain information about a product from a single data node. However, in many industrial applications, information about a single product can be distributed in multiple different data nodes, and aggregating the information from these nodes has become a common task. In this paper, we provide a distributed service-oriented architecture for this task. In this architecture, each manufacturer provides service for their own products, and data nodes keep the information collected by themselves. Semantic technologies are adopted to handle problems of heterogeneity and serve as the foundation to support different applications. Finally, as an example, we illustrate the use of this architecture to solve the problem of product tracing.

We apply cybernetics theory to control software behaviors in architecture-level.We propose Breeze/ADL to specify the self-adaptive architecture.We design a learning algorithm to derive feedbacks to adjust the architecture.We extend tool - Breeze to support our framework. Display Omitted The cloud computing era requires software architecture to be self-adaptive to the dynamic environment. This autonomous feature brings uncertainty and makes software behavior difficult to control. The uncontrollable behavior is caused by ill-defined architecture and might lead to system disruption. To address this problem, we propose a novel framework which applies software cybernetics to guide self-adaptive architecture evolution. In our framework, we formulate the architecture evolution process as a feedback control process. In the process, we take the self-adaptive architecture model and the model checking technique as the controlled object and controller, respectively. First, the self-adaptive architecture is specified by Breeze/ADL. Second, the framework leverages model checking to validate adaptive Breeze/ADL specifications. Third, a learning algorithm is designed to regulate validation results to generate feedback rules - Productions to guide the architecture evolution. A smart phone application example is chosen to demonstrate the feasibility of our framework. The results show that our framework facilitates architects to detect undesired states which are caused by error-prone adaptation rules.

Clustering-based software architecture recovery is an area that has received significant attention in the software engineering community over the years. Its key concept is the compilation and clustering of a system-wide graph that consists of source code entities as nodes, and source code relations as edges. However, the related research has mostly focused on investigating different clustering methods and techniques, and consequently there is limited work on addressing the question of what is a minimal set of relations that can be easily extracted from the system's source code, and yet can be accurately used for extracting its architecture. In this paper, we report on results obtained from an architecture recovery case study we have conducted, by considering all possible combinations which can be generated from thirteen commonly used source code relations. We have examined the similarity of the extracted architectures obtained by using each different relation combination for different systems, against the corresponding architecture which is obtained by applying all thirteen relations and whch we consider as the ground truth architecture. For this purpose, we have also examined whether the use of all these thirteen relations is indeed adequate to yield a ground truth architecture, by applying this architecture extraction process on five large sofware systems for which their ground truth architecture has been independently established. The overall results of our study indicate that there is small set of relations for procedural systems, and another similar set for object oriented systems, that can be easily extracted from the source code and yet used to yield an architecture that is close to the ground truth architecture.

In modern manufacturing, the product architecture design options are usually restricted to those that can be produced with 100% confidence using those proven technologies to satisfy the existing customer requirement. As a result, the inefficiencies of architecture design are considerable due to such limitations. This issue is of particular interests in cyber manufacturing when exploring the tradeoff between generality and feasibility in product design and manufacturing. It can be expected that the improvement and extension of the existing product architecture may be required to meet new customer requirement when new technologies become available. An effective system performance assessment algorithm is necessary to facilitate the extension of existing product architecture. Though there has been a lot of research on architecture assessment, there is no well-defined model for level by level architecture assessment considering architecture extension. In this paper, we propose a general architecture assessment model considering the integration of additional functionality requirements and performance metrics to evaluate the architecture performance along its value pathway to meet stakeholder’s requirements. A numerical case study focusing on a hypothetical auto cooling system is used to validate the effectiveness of the proposed model.

Big Data OverviewDriven by the need to generate business value, the enterprise has started to adopt Big Data as a solution, migrating from the classical databases and data stores which lack the flexibility and are not optimized enough [1].The changes in the environment make big data analytics attractive to all types of organizations, while the market conditions make it practical. The combination of simplified models for development, commoditization, a wider palette of data management tools, and low-cost utility computing has effectively lowered the barrier to entry. [2]. The concept addresses large volumes of complex data, rapid growing data sets that may come from different autonomous sources.In recent approaches, Big Data is characterized by principles known as the 4VVolume, Variety, Velocity and Veracity [3]. There are opinions about accepting other principles as Big Data characteristics, such as Value.Each day more businesses realize that Big Data is relevant as the applications generate large volumes of data generated automatically, from different data sources, centralized or autonomous. As traditional databases hit limitations when the need of analyzing this data, dedicated solutions must be considered.Important BigData Solutions:* Apache HBase/Hadoop is based on Google's BigTable distributed storage system, which runs on top of Hadoop as a distributed and scalable big data store. This means that HBase can leverage the distributed processing paradigm of the Hadoop Distributed File System (HDFS) and benefit from Hadoop's MapReduce programming model. It combines the scalability of Hadoop with real-time data access as a key/value store and deep analytic capabilities of Map Reduce [4].HBase allows to query for individual records as well as derive aggregate analytic reports across a massive amount of data. It can host large tables with billions of rows, millions of columns and run across a cluster of commodity hardware. HBase is composed of three types of servers in a master slave type of architecture. Region servers are responsible to serve data for reads and writes. When accessing data, clients communicate with HBase Region Servers directly. Region assignment, DDL (create, delete tables) operations are handled by the HBase Master process.* Apache Cassandra is a distributed database used for the administration and management of large amounts of structured data across multiple servers, while providing highly available service and no single point of failure. It provides features such as continuous availability, linear scale performance, data distribution across multiple data centers and cloud availability zones. Cassandra inherits its data architecture from Google's BigTable and it borrows its distribution mechanisms from Amazon's Dynamo.The nodes in a Cassandra cluster are completely symmetrical, all having identical responsibilities. Cassandra also employs consistent hashing to partition and replicate data. it has the capability of handling large amounts of data and thousands of concurrent users or operations per second across multiple data centers.Cassandra has a hierarchy of caching mechanisms and carefully orchestrated disk I/O which ensures speed and data safety. Write operations are sent first to a persistent commit log (ensuring a durable write), then to a write-back cache called a memTable; when the memTable fills, it is flushed to a sorted string table - SSTable - on disk. A Cassandra cluster is organized as a ring, and it uses a partitioning strategy to distribute data evenly.* Redis represents an in-memory data structure store used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperlogs and geospatial indexes with radius queries. Redis stores all data in RAM, allowing lightning fast reads and writes. It runs extremely efficiently in memory and handles high-velocity data, needing simple standard servers to deliver millions of operations per second with submillisecond latency. …

Most large enterprises requiring operational business processes utilize several thousand instances of legacy, upgraded, cloud-based, and/or acquired information management applications. With the advent of Big Data, Business Intelligence (BI) systems, receive unconsolidated data from a wide-range of data sources with no overarching governance procedures to ensure quality and consistency. Although different applications deal with their own flavor of data, reference data is found in all of them. Given the critical role that BI plays in ensuring business success, the fact that BI relies heavily on the quality of data to ensure that the intelligence being provided is trustworthy, and the prevalence of reference data in the information integration landscape, a principled approach towards management, stewardship and governance of reference data becomes necessary to ensure quality and operational excellence across BI systems. The authors discuss this approach in context of typical reference data management concepts and features, leading to a comprehensive solution architecture for BI integration.

Virtual instruments is a program that implements functions of an instrument by software which could replace the work of real instruments to save resources. The functions of these sensor-based systems are limited and they commonly cannot manage related information, such as sensors and monitoring objects, due to special requirements. The procedure of development and integration often suffers from low efficiency because of non-standard technologies. To solve the aforementioned problems, an integrated system architecture based on complex virtual instruments (CVI) is proposed, which could not only extend the function of virtual instruments but also ease the development procedure of the system. By analyzing the characters of existing virtual instruments systems, this paper presents the data interaction standard, which has been applied by two IEEE standards, and the architecture, which consists of configuration subsystem, data collection simulation subsystem, Web service registration center, and so on. We also offer a light universal client which could dynamically load the DynamicLinkLibrary of different CVIs, in order to replace the complex integration procedure by scheduling different components. To valid the architecture, three existing systems are reconstructed by the proposed prototype subsystems. The result shows that the proposed architecture is efficient and feasible.

The Internet of Things has been widely deployed in various areas of daily life through heterogeneous communications protocols. Each unstandardized protocol focuses on a specific IoT communication pattern. Inspired by the Internet architecture over which divergent devices can easily be accessed and a considerable number of applications can be run, we propose a generic architecture for IoT. This architecture supports two DIY areas: network DIY for data aggregation and application DIY for service cooperation. To connect these two DIYs, a centralized controller has been designed to provide standardized interfaces for data acquisition, organization, and storage, and to support elastic and supportive computing. With these properties, divergent devices can coexist in a uniform microworld, and rich services can be developed and provided on demand to interoperate with physical devices. This article discusses the background, design principles, and advantages of the proposed architecture, as well as open problems and our initial solution, which substantiates a novel IoT architecture and new research ground.

The paper discusses an external solution for data quality management in information systems. In contradiction to traditional data quality assurance methods, the proposed approach provides the usage of a domain specific language (DSL) for description data quality models. Data quality models consists of graphical diagrams, which elements contain requirements for data object's values and procedures for data object's analysis. The DSL interpreter makes the data quality model executable therefore ensuring measurement and improving of data quality. The described approach can be applied: (1) to check the completeness, accuracy and consistency of accumulated data; (2) to support data migration in cases when software architecture and/or data models are changed; (3) to gather data from different data sources and to transfer them to data warehouse.

none 5 no Starting from the definition of the Internet-of-Things (IoT) paradigm, this paper discusses goals and strategies for the design and building of an IoT architecture aiding the planning, management and control of the Food Supply Chain (FSC) operations. A comprehensive architecture of the entities, the physical-objects, the physical and informative flows, the stages and the processes to be sensed, tracked, controlled and interconnected is given to illustrate the interdependencies between the observed supply chain and the exogenous environment. A simulation gaming tool embedding the IoT paradigm for the FSC management is also proposed and illustrated to showcase the potential benefits and opportunities for more direct integration of the physical food ecosystems into virtual computer-aided control environment. Accorsi, Riccardo; Bortolini, Marco; Baruffaldi, Giulia; Pilati, Francesco; Ferrari, Emilio Accorsi, Riccardo; Bortolini, Marco; Baruffaldi, Giulia; Pilati, Francesco; Ferrari, Emilio

Nowadays, data is being generated, collected, and analyzed at an unprecedented scale, data integration is the problem of combining data from heterogeneous, autonomous data sources, and providing users with a unified view of integrated data. To design a data integration framework, we need to address challenges, such as schema mapping, data cleaning, record linkage, and data fusion. In this paper, we briefly introduce the traditional data integration approaches, and then, a novel graph-based data integration framework based on unified concept model (UCM) is proposed to address real-world refueling data integration problems. Within this framework, schema mapping was carried out and metadata from heterogeneous sources is integrated in a UCM. UCM has the benefits of being easy to update. It is also important for effective schema mapping and data transformation. By following the structure of UCM, data from different sources is automatically transformed into instance data and linked together by using semantic similarity computation metrics, finally the data is stored in graph database. Experiments are carried out based on heterogeneous data from refueling records, social networks of astroturfers, and vehicle trajectories. Experimental results and reference implementation demonstrations show good precision and recall of the proposed framework.

Commodity flow modeling studies rely on traditional data sources, such as the Commodity Flow Survey, the Freight Analysis Framework, Transearch, surveys, the U.S. census, county business patterns, and input–output models. The strengths and shortcomings of those data sources have been evaluated in the literature; the sources can be useful for modeling, but they do not necessarily support a supply chain approach or provide the level of detail or accuracy desired for modeling a particular commodity’s supply chain and flow on a city or state roadway network. This paper expands on the work of NCFRP Report 35: Implementing the Freight Transportation Data Architecture: Data Element Dictionary by finding existing data sources unique to specific commodities that identify key supply chain locations and industry relationships and that provide more detail about commodity quantity and movement to overcome the limitations of traditional freight data sources. The goal of the investigation was to find more data sets to use in commodity flow modeling. For each commodity, this paper describes data sources found, data attributes, and how those data were used to estimate flow from origins and destinations within supply chain links. The commodity-specific approach opens doors to sources of data not normally incorporated into transportation research.

The concept of the smart city is widely favored, as it enhances the quality of life of urban citizens, involving multiple disciplines, that is, smart community, smart transportation, smart healthcare, smart parking, and many more. Continuous growth of the complex urban networks is significantly challenged by real-time data processing and intelligent decision-making capabilities. Therefore, in this paper, we propose a smart city framework based on Big Data analytics. The proposed framework operates on three levels: (1) data generation and acquisition level collecting heterogeneous data related to city operations, (2) data management and processing level filtering, analyzing, and storing data to make decisions and events autonomously, and (3) application level initiating execution of the events corresponding to the received decisions. In order to validate the proposed architecture, we analyze a few major types of dataset based on the proposed three-level architecture. Further, we tested authentic datasets on Hadoop ecosystem to determine the threshold and the analysis shows that the proposed architecture offers useful insights into the community development authorities to improve the existing smart city architecture.

Currently, context awareness has become essential in software applications and services owing to the high demand by users, especially for mobile computing applications. This need to provide context awareness requires a software infrastructure not only to receive context information but also to make use of it so that it provides advantageous services that may be customized according to user needs. In this paper, we provide an event-driven service-oriented architecture supported by an enterprise service bus, which will facilitate the incorporation of Internet of Things data and provide real-time context-aware services. The result, which has been validated through a real-world case study, is a scalable context-aware architecture which can be applied in a wide spectrum of domains.

Most existing non-real-time applications utilize infrastructure-based or semi-infrastructure-based network architectures. Such a network architecture demands a considerably high installment and maintenance cost. To alleviate the cost, in this article, we propose an efficient infrastructure-less network architecture named CrAN. In CrAN, a set of crowds play significant roles by completing the communication gaps among various associates in the network; hence the name. We show the usability of this proposed architecture to support non-real-time data transmission over an SGMS, where optimum solutions need to be discovered to minimize the management cost. Due to the complexity of the optimization problem, we approximate these optimum solutions using a GA. In the implementation of the GA, we apply new fitness functions to discover a feasible trade-off between distance and waste volume. We then compare the performance of the proposed fitness functions with that of an existing fitness function. The results favorably suggest the necessity of employing the proposed fitness functions to obtain near-optimum solutions.

This paper presents a software architecture for micro-controllers based solutions that run in capture data cards for Internet of Things (IoT). The present approach describes the components of the software architecture and its interaction. Equally, the architecture allows the development of modular and configurable applications as is focused on the overall design and system specification. The evaluation was performed in a Fog Water Collection system.

System-on-chip (SoC) face major problem due to vulnerability of hack. The hacker target the cryptographic IP block in the architecture of SoC. However, PUF test wrapper provides the security for individual IP core. The individual IP core protection plays major problem in PUF test. We propose a novel method to protect the IP core with QFT-PUF authentication mechanism. QFT-PUF implement in PSOC-FPGA. The mechanism reduces the area and memory in architecture. The proposed method of key generation and their handling process drive from Quantum Fourier Transform. From the validation of QFT-PUF, Fault Acceptance Rate (FAR) increases then the Fault Rejection Rate (FRR).

How does one start a new business online without thinking about its profit? Numbers of retailers with limited research, or asking enough details and questions, to make sure their product is something that is more likely to be sold and if does one considers enough storage to cater each customer needs. Why do facebook and amazon require Big Data in their business online? Right now, aspiring entrepreneurs are planning their paths to a more productive business using social commerce and taking it to the next level. It's a journey that requires a lot of hard work, and many people end up failing, but the business that is taking risks will end up reaping the cream of their crops. In this paper, we discuss the process of improving the production of an online business using big data in social media to increase sales and operating margin. That retailer, using Big Data to its current potential could gain a competitive edge to possibly gain opportunities for business and customers. Thus, we identify the advantages of using big data in social commerce.We also include the Java-based programming framework (Hadoop) that supports the processing and storage of a larger data set in a distributed computing environment. Naming it as next-generation enterprise data architecture that links the systems business transactions and business intelligence.

In view of growing importance of data, information and knowledge in companies has become very actual issue of the quality of their processing through the information system modules. Using of modules of the management information systems for qualified analyzes conducted over primary data stored in companies in the Czech Republic is not too widespread. The aim of this article is based on a long-term investigation conducted to analyze the situation and propose for agricultural holdings architecture management information system for farmers to support their decision-making activities.

This paper aims at introducing a type of social commerce architecture to which the name Interaction Commerce has been given. First, a global description of the main macro-components forming the structure of this architecture is provided. Such components also take care of managing e-commerce activities and social relationships within the architecture. Second, the focus is set on the analysis of the single components that are key to the social aspects of the architecture. A special chapter is then entirely focussed on a topic that is considered extremely important by the entire research community, i.e., recommender systems. After providing a general introduction on the topic, the two most common recommendation approaches are analyzed and compared. These are the content-based approach and the collaborative filtering approach. The analysis has shown how all recommender systems are threatened by the cold-start problem. Studying recommender systems has allowed for their implementation in the architecture, which now has a new “social” approach that is able to solve the new user cold-start problem. An architecture prototype was developed and tested in order to be validated. To cite this document: Luca Salvatori and Fausto Marcantoni, "Interaction commerce, a technological architecture focused on recommender system", International Journal of Electronic Commerce Studies, Vol.7, No.2, pp.105-134, 2016. Permanent link to this document: http://dx.doi.org/10.7903/ijecs.1443

One big challenge in building a smart grid arises from the fast growing amount of data and limited communication resources. The traditional centralized communication architecture does not scale well with the explosive increase of data and has a high probability of encountering communication bottlenecks due to long communication paths. To address this challenging issue, this article presents a distributed communication architecture that implements smart grid communications in an efficient and cost-effective way. This distributed architecture consists of multiple distributed operation centers, each of which is connected to several data concentrators serving one local area and only sends summary or required integrated information to a central operation center. Using this distributed architecture, communication distance is much shortened, and thus data will be delivered more efficiently and reliably. In addition, such a distributed architecture can manage and analyze data locally, rather than backhauling all raw data to the central operation center, leading to reduced cost and burden on communication resources. Advanced metering infrastructure is chosen as an example to demonstrate benefits of this architecture on improving communication performance. The distributed communication architecture is also readily applicable to other smart grid applications, for example, demand response management systems.

As the growing of applications with big data in cloud computing become popular, many existing systems expect to expand their service to support the explosive increase of data. We propose a data adapter system to support hybrid database architecture including a relational database (RDB) and NoSQL database. It can support query from application and deal with database transformation at the same time. We provide three modes of query approach in data adapter system: blocking transformation mode (BT mode), blocking dump mode (BD mode), and direct access mode (DA mode). We provide a data synchronization mechanism and describe the design and implementation in detail. This paper focuses on velocity with proposed three modes and partly variety with data stored in RDB, NoSQL database and temporary files. With the proposed data adapter system, we can provide a seamless mechanism to use RDB and NoSQL database at the same time. This paper presents data adapter to make possible the automated transformation of multi-structured data in Relational Database (RDB) and NoSQL systems.With the proposed data adapter, a seamless mechanism is provided for constructing hybrid database systems.With the proposed data adapter, hybrid database systems can be performed in an elastic manner, i.e., access can be either RDB or NoSQL, depending on the size of data.

– OAIS (Open Archival Information System) is the digital preservation standard practiced world wide by repositories and archives that perform long-term digital preservation. – The CCSDS DAI standards committee developed the OAIS – We (DAI) are now developing interoperability standards to support the OAIS processes. – First we discuss OAIS material for context. – Then we explain the technical approach of the OAIS Interoperability Framework (OAIS-IF) o Standardized interfaces for producers and consumers enable usability, re-use, authenticity and preservation. o An abstraction layer provides independence from the implementation details of the underlying archive o "Plug-Ins" are installed by the user depending on what archive/discipline he wants to access. (Approach may include protocols, bindings, APIs, etc.) – The result is a flexible interoperable architecture for users and archive-to-archive transactions that better enables usability and preservation. – The ability to install plug-ins for various disciplines opens a broad ability for cross -discipline research/convergence. – As usage of OAIS is ubiquitous, similarly ubiquitous implementation of OAIS-IF will improve long-term access to preserved assets well into the distant future. – We invite critique of our technical approach, and we invite participation from organizations and individuals, particularly those that can bring solid technical skills to bear on the data architecture and protocol problems.

The International Brain laboratory (IBL) is a collaboration aiming to understand the neural basis of decision-making. Ten experimental labs use multiple neural recording modalities in diverse brain structures of mice making perceptual decisions. A primary requirement of IBL is to establish a data architecture that integrates data from all labs and modalities together. We have developed a system that allows users across 5 countries to automatically contribute data and metadata, search for relevant data, and share code for exploratory analysis. To accurately record metadata about subjects and experiments in a searchable and accessible way, we have developed a user-friendly, web-based electronic lab notebook system for colony and experiment management (Alyx). Alyx is a small relational metadatabase that records relevant information about each subject (age, strain, procedures), alongside information about experiments and their resulting data files. Once an experiment is completed, data registered in Alyx is automatically uploaded to a central server via Globus transfer. Users can search and access the data using a lightweight interface called the Open Neurophysiology Environment (ONE), implemented in Python and MATLAB. ONE defines a list of DatasetTypes, as arrays of predetermined shapes and units. Users search the database by running a command that returns an ID identifying experiments matching their criteria. A command one.load returns the requested DatasetTypes as a numerical array, downloading it from the central server and caching on the users machine to avoid repeated downloads. Additional commands provide further ways to list experiments on the server, and the dataset types they each contain. This system allows users to search, load and process data that was collected in laboratories spanning multiple geographical locations. To visualize these data, we have developed a pipeline for automated analysis, based on the DataJoint framework.

The aim of this paper is to set out the principles of an ideal data system. Good data is crucial to effective policy and practice development in all social policy spheres and this is a particular challenge in the context of homelessness policy. Policy makers, practitioners and researchers have been highly critical of the current state of homelessness data across the globe, with concerns largely focused on the incompleteness of the data. Most research has narrowly focused on the strengths and weaknesses of different data collection techniques, such as Point-In-Time counts. However, good data does not only derive from the data collection method - consideration must also be given to the wider data system, including how data are generated, reported, analysed, and crucially, how they are made accessible and to who. The evidence base for the paper is a desk-based review of 49 data collection systems from 8 countries, including systems in health and social care settings—where data are being increasingly used to drive more effective care. The different systems are synthesised to generate 8 areas of design, being: data architecture, governance, data quality, ethical and legal, privacy/security, data access, and importantly, purpose. Drawing these elements together, the paper concludes that data collection should adopt a common data standard shared across the sector, enabling inter-organisational information sharing and improving collaboration; reporting to local and central government must not be one-sided, instead data providers should receive some tangible benefit for their engagement; the focus of analysis needs to shift from statistics toward evaluation into the effectiveness of interventions; and access must be available to a range of sector actors, including service providers and academia. Importantly, the paper also concludes that in delivering the ideal system, care must be taken not to interrupt the delivery of effective homelessness interventions.

Distributed ledger technology (DLT) is a database architecture which enables the keeping and sharing of records in a distributed and decentralized way, while ensuring its integrity through the use of consensus-based validation protocols and cryptographic signatures. In principle, DLT has the potential to reduce costs and increase the efficiency of securities settlement, the ultimate step of every security transaction. In this paper, we first examine to what extent DLT could add value and change securities settlement. We then characterize the innovation process in the post-trade industry and finally, we describe the economics of a hypothetical DLT-based security settlement industry. Our main conclusions are that: i) DLT has the potential to improve efficiency and reduce costs in securities settlement, but the technology is still evolving and it is uncertain at this point what form, if any, a DLT-based solution for securities settlement will ultimately take, ii) technological innovation in the post-trade industry is more likely to achieve its potential with some degree of co-ordination which could be facilitated by the relevant authorities, and iii) if DLT-based securities settlement becomes a reality, then it is likely to be concentrated among few providers which, in the absence of regulation, could result in inefficient monopoly pricing or efficient price discrimination with service providers capturing much of the market surplus.

This paper elucidates electronic invoice system’s database architecture requirements and its building issues that one may encounter. Enumerated requirements and issues were taken into consideration and it is suggested to use three types of DB in the system. NoSQL model of database for its numbers of advantages is recommended for the information reused by users to store and querying them. The database is able to split database loadequally with the help of horizontal sharding. Making partition numbers equal to the number which has common divisors as much as possible will let splitting load amount equally among possible additional infrastructure. A special approach is required to each element of system to perfectly create architecture of the information system's database.

Background Although clinical decision support (CDS) alerts are effective reminders of best practices, their effectiveness is blunted by clinicians who fail to respond to an overabundance of inappropriate alerts. An electronic health record (EHR)–integrated machine learning (ML) algorithm is a potentially powerful tool to increase the signal-to-noise ratio of CDS alerts and positively impact the clinician’s interaction with these alerts in general. Objective This study aimed to describe the development and implementation of an ML-based signal-to-noise optimization system (SmartCDS) to increase the signal of alerts by decreasing the volume of low-value herpes zoster (shingles) vaccination alerts. Methods We built and deployed SmartCDS, which builds personalized user activity profiles to suppress shingles vaccination alerts unlikely to yield a clinician’s interaction. We extracted all records of shingles alerts from January 2017 to March 2019 from our EHR system, including 327,737 encounters, 780 providers, and 144,438 patients. Results During the 6 weeks of pilot deployment, the SmartCDS system suppressed an average of 43.67% (15,425/35,315) potential shingles alerts (appointments) and maintained stable counts of weekly shingles vaccination orders (326.3 with system active vs 331.3 in the control group; P=.38) and weekly user-alert interactions (1118.3 with system active vs 1166.3 in the control group; P=.20). Conclusions All key statistics remained stable while the system was turned on. Although the results are promising, the characteristics of the system can be subject to future data shifts, which require automated logging and monitoring. We demonstrated that an automated, ML-based method and data architecture to suppress alerts are feasible without detriment to overall order rates. This work is the first alert suppression ML-based model deployed in practice and serves as foundational work in encounter-level customization of alert display to maximize effectiveness.

This paper takes the dearth of quality procurement data as an empirical point of departure to assess emerging regulatory trends in data-driven and digital public procurement governance and, in particular, the European Commission’s ambition for the single digital procurement market. It resorts to two well-known elephant tales to send a message of caution. It first appeals to the image of medieval bestiary elephants to stress the need to develop a better data architecture that reveals the real state of the procurement landscape, and for the European Commission to stop relying on bad data in the Single Market Scoreboard. The paper then assesses the promises of blockchain and smart contracts for procurement governance and raises the prospect that these may be new white elephants that do not offer significant advantages over existing sophisticated databases, or beyond narrow back-office applications—which leaves a number of unanswered questions regarding the desirability of their implementation. The paper concludes by advocating for EU policymakers to concentrate on developing an adequate data architecture to enable digital procurement governance.

The basic situation of NQI (National Quality Infrastructure) in the field of smart grid measurement is introduced. Aiming at the information requirement of NQI in the field of smart grid measurement, the construction scheme and purpose of NQI one-stop service cloud platform are proposed. The technical architecture of cloud platform is studied, the operation and security management of cloud platform are introduced. The data architecture of cloud platform is studied, the data access scheme and data acquisition method are designed in accordance with the current business situation, and the data model based on NQI is proposed. The application architecture of cloud platform is studied, and the service design of cloud platform is discussed according to the application goal of cloud platform. Finally, the further development of cloud platform and its application in real business are prospected.

This study discusses the design of information technology enterprise architecture in the cooperation of Budi Karya. The main problem encountered is that the management still uses a bookkeeping system. The information obtained in each section has not been integrated with other, the IT management organization structure is not sufficient because there is no staff who have specialized IT experts, and require development in information technology. This was stated in one of the Budi Karya cooperation missions which read "Adopting the development of information technology and modern management systems". This study aims to produce process architecture, data, applications and technology that can be a reference for implementing all activities in cooperation of Budi Karya. This research uses the TOGAF (the Open Group Architecture Framework) which is a framework that has detailed methods and tools for developing and implementing enterprise architecture, with 8 architectural phases of vision, business architecture, information system architecture, technology architecture, opportunities and solutions, migration plan, implementation Governance, and architecture change management. Data collection is done by direct observation interviews and literature studies. The results of all these phases will produce a detailed framework of process architecture, data architecture, application architecture, and architecture in cooperation of Budi Karya Keywords: Enterprise, Architecture, Cooperation, framework, TOGAF

Enterprises today are technology driven and comprise of plethora of applications that may be categorized based on the technology that they are developed and deployed on. For enterprises that have existed across years and across multiple business cycles, the technologies may be classified as legacy, mature or emerging. The challenge lies in interoperability within and without the organization, especially with respect to the business objects that are required across business functions, to realize the capabilities of the organization. This is also true for scenarios of M&As (Mergers & Acquisitions) and also during creation of JVs (Joint Ventures).Enterprise Architecture (EA) defines the Business-Technology alignment in organizations, and is an established methodology for business transformation and establishing enterprise maturity in the keenly competitive business world. Business objects are defined as Data Architecture artifacts within the ambit of EA.The challenges to business object interoperability arises due to the incompatibility of technologies used by the applications. This leads to the well explored n*(n-1) scenario, where n is the number of application interfaces. This has serious implications towards business health of the organization, and risk to the BAU (Business As Usual) of the organization. This is because in a complex mesh like n*(n-1) scenario, it becomes practically impossible to identify the impact of changes to business capabilities in an inconspicuous attribute of a business object in an application domain.Thus the impact analysis of business objects / data as defined by traditional description is a challenge to business sustainability of organizations. These challenges in data architecture impact analysis may be mitigated by the AI (Artificial Intelligence) paradigm, by taking recourse to the very powerful features of AI, by defining predicate calculus based knowledge bases.In our paper we consider the Banking domain for carrying out our discussions.

International audience; In the context of Industry 4.0, Cyber-Physical Production Systems (CPPS) and digital twins are key technologies for the management of huge amount of data generated by Industrial Internet of things (IIoT) devices. However, it is still an issue to make heterogeneous components interoperable and make them flexible to each industrial specific needs. Thus, this paper aims to identify the components and needs of Industry 4.0 and proposes a database architecture allowing to fulfill these requirements. The proposed architecture is implemented on a cyber-physical production system (CPPS) and its applicability is showed and discussed through several use cases.

The SmartParticipation platform aims to fulfill the five levels of participation presented in Chap. 6. In this chapter, the reader is presented with a general overview of the technologies, GUI, and features proposed for this platform. Additionally, a number of modules that can be used to fulfill the five participation levels are listed.

Building Information Modelling (BIM) is one of the most significant technological advancements in recent years that has been adopted by the design and construction industry. While BIM adoption is growing, it can be witnessed that adoption is relatively weak within operational and maintenance (O&M) organisations such as estate and infrastructure management, who would ultimately gain the highest value of utilising BIM. While the challenges of BIM adaptation are multifaceted, there is a recurring theme of poor data integration between BIM and existing information management systems. There is a clear gap of knowledge on how to structure a BIM model that allows for efficient use within the O&M phase. Furthermore, there are no standard processes that allow for the exchange from a BIM model into an Asset Information Model (AIM). This paper outlines a process that enables extraction of BIM related data directly from a model into a relational database for integration within existing engineering asset management information management systems. The paper describes the BIM model requirements, development of the extraction platform, database architecture and framework. Furthermore, a case study is presented that demonstrates the methodology. The case study shows that if the BIM model is designed from the start with consideration for the O&M requirements, it can be exploited for development into an AIM. Furthermore, it demonstrates that a structured approach to object classification within a BIM model support the efficient exchange of data directly from the BIM model

As the world is frantically moving towards digitization, handling huge volumes of data is becoming complex and difficult to optimize. This is mainly because of increasing dimensions of data, its unstructuredness and increased need for storage space. To increase the efficiency of the cloud environment, many service providers have moved towards multi-tenant architecture. In a multi-tenant architecture, a number of individual applications termed as tenants work under a shared environment. In such a scenario, it is imperative that data of one tenant be isolated from that of other tenants. Further, it is also desirable to optimize the use of storage space. With these considerations, this paper proposes a multi-tenant database architecture for applications using a column-based NoSQL data store. The architecture has been implemented with the NoSQL database, Cassandra.

The implementation of systems and information technology has become the company's main strategy to remain able to compete in an increasingly competitive business climate. Information systems have a role to help manage business processes effectively and efficiently. Case studies on PT XYZ companies show that the use of IS / IT has not been optimal in supporting the sustainability of its business processes. This is due to the absence of information technology architecture planning that is aligned with the company's business strategy, especially related to the Enterprise Architecture (EA) which consists of Business Architecture, Data Architecture, Application Architecture and Technology Architecture. Business processes and organizational activities. This study aims to design cloud computing-based Enterprise Architecture (EA) for PT. TELIN using the TOGAF ADM method as a framework. The integration between Enterprise Architecture (EA) and cloud computing brings great benefits to the company. Cloud Computing provides elasticity in software and hard solutions in organizations, while Enterprise Architecture (EA) guarantees compatibility between business strategies and IT strategies. This study produced the architectural design of the company PT. TELIN is done by looking at business strategies in the process of production and product distribution with the aim of supporting the implementation of the company's information system as a whole, resolving the problem of information system integration, and increasing effectiveness and efficiency in the implementation of information technology infrastructure.

Data governance is a basic foundation that must prepare to make data management, and data architecture is a fundamental thing in data management. Currently, there is no data management in the Ministry of Foreign Affairs of the Republic of Indonesia which cause problems. The purpose of this study is to design data governance structure and data architecture for the Ministry of Foreign Affairs of the Republic of Indonesia. The research method used for this research is a case study. This study used analysis, interview, discussion and benchmark to another government institution (KKP and BAPPENAS) for collecting and processing data. To conduct research analysis from hidden problems, the steps from soft system methodology and PDCA lifecycle was used. DMBOK framework was used as a guide to define data governance structure and data architecture.

The future cyber society is a virtual world made of data, contrasted with the real world made of material. Human beings are already living in these two interacted and fusional worlds. Various of data are not only the kind of valuable resources, but also the new cognitions of methodology from the viewpoint of data. There are too many characteristics and attributions of data we even didn't really know, such as data philosophy, data thinking, data theory, data rules, data assets, data ownership, data protection, data sharing, data application, data method, data architecture, etc. We need to build an open, safety, sharable, ecological data platform to manage all kinds of data and support various applications for the future cyber society. A simple architecture of data-oriented and data ownership-based, constructed of one-body with two-wings for building complex information systems was proposed and it may be suitable for the future ordered cyber data society.

According to survey that was being held by DailySocial to 2030 smartphone users in Indonesia, more than 91 percent of respondents have booked flight tickets via online with more than 86 percent of them have booked through online travel agency (OTA). Flylist is the OTA that has been created by PT Solusi Awan Indonesia and published on August 2018 with the vision to retrieve 20 percent of the cheapest price that has not been sold by premium and non-premium airlines and reduce the risk of losing the chances of selling, refund, and overbooking processg. Flylist’s vision is relatively huge and will be difficult for new system. To achieve it, data maturity and management should be assessed. Paper will discuss OTA theory, framework (DMBOK and other framework by James M. B.), IATA theory and inovation, research data from interviewees, and document study that will be assessed with HESA data maturity assessment.Based on data maturity assessment, we found that overall score is level 3 (proactive) with the result is Flylist can be improved by doing recommended data governance, standards & compliance, data architecture management and NDC theory on customer service contact, communication, and business intelligence system, technology, data security management activity.

A new vision in semantic big data processing is to create enterprise data hubs, with a 360° view on all data that matters to a corporation. As we discuss in this paper, a new generation of multi-model database systems seems a promising architectural choice for building such scalable, non-native triple stores. In this paper, we first characterize this new generation of multi-model databases. Then, discussing an example scenario, we show how they allow for agile and flexible schema management, spanning a large design space for creative and incremental data modelling. We identify the challenge of generating sound triple-views from data stored in several, interlinked models, for SPARQL querying. We regard this as one of several appealing research challenges where the semantic big data and the database architecture community may join forces.

Everyday more scientific papers are published, and science grows. However, the amount of information required for students to keep updated seems overwhelming. From the ever-present smartphone to the new wearables, mobile devices are everywhere and taking more and more part in our lives. Our proposal is enriching the framework of these devices (smartphones, laptops or even e-book readers) to help students to keep updated in different ways to deliver scientific content in a more personal and comfortable form. Many techniques can be used in order to enhance the user interest, readability and information retention and the way and form the information should be displayed is important. Technologies such as text-to-speech may offer complimentary help or replace some parts. Videos may also help deliver the information fast, so the user is more focused in a short period of time. How to create these from the information available in scientific papers is precisely explored in this paper. What is more, personal data can be collected, such as research interests or previous readings, which can provide important information on how to adapt certain techniques to the user for a better result. An early state of this data architecture is shown. This paper aims to show current techniques being used and explore possibilities provided by mixing different techniques and strategies and put them to test with the use of technology. In order to do so, work will be focused on developing an architecture that can evaluate readers’ performance and get feedback from multiple reading scenarios. The architecture will be presented showing its different modules. Each module will be explained and how it can solve a step of the problem. The methodology used allows to replace modules as improved modules with different techniques are found through user feedback.

It has so far been unclear which data-intensive CPU tasks can be accelerated with GPUs, as GPUs are bottlenecked by the slow bus connection to the CPU and the limited size of GPU memories.In this paper we demonstrate a database workload where co-processing actually helps: accelerating large join pipelines where the join condition is selective, by pushing down a Bloom filter test for early pruning. GPUs are more powerful than CPUs for computing hash functions needed in Bloom filter tests, have a local memory with significantly more random-access bandwidth than the CPU, and since only keys (or extracts thereof) have to be moved to the GPU, data transfers over the bus are relatively small. Our micro-benchmarks show that raw Bloom filter lookups are up to 6x faster on the GPU than on the CPU in case the Bloom filter is larger than the CPU cache.The next quest is for a database architecture that allows efficient CPU-GPU co-processing. We present a new heterogeneous query processing framework based on fluid co-processing. In fluid co-processing, tasks of different sizes -- that fit the device -- are dynamically co-processed. Early results show that fluid co-processing consistently improves end-to-end CPU performance of early pruning in join queries thanks to the GPU, by factors up to 2-3x.

Drilling system software is crucial for the complex geological drilling process due to its vital role in improving the drilling efficiency and safety. In this paper, a Model-View-Controller (MVC) pattern-based software framework and data architecture of an intelligent drilling system are proposed. First, four main functional requirements, namely, data fusion and visualization, safety assessment and efficiency calculation, efficiency and safety optimization, and coordination control have been analyzed. After that, a four-layers is established based on the above analysis and MVC pattern. Finally, a new online data architecture has been presented at the end of this paper. The proposed software framework and data architecture form a basis for the monitoring of the drilling process.

Recommendation system requires supporting data that sometimes are heterogeneous either from its sources or from its data format. In the context of tourism recommendation system, a previous research resulted in the concept of collecting the Tourism Contextual Information (TCI) data to determine the relevant information as the support of tourism recommendation system. However, what things and how the data were collected have not been explained yet. By adopting the global Extract Transform Load (ETL) process, this research aims to show how the supporting data including the database structure, the data architecture, and the data representation in the tourism recommendation system are collected. The result of the research can be used for the need of further research – particularly on the tourism recommendation system considering the change of weather or traffic condition and its effect on making decision when planning a travel or a tour.

Herein, we develop a probabilistic methodology that enables the application of semi-supervised learning over a data architecture for knowledge representation. The data architecture, proposed here, is known as the Topological Hierarchal Decomposition (THD) and is derived from the use of topological compression to decompose data into subsets of increasing attribute similarity. We demonstrate the use of the THD and a probabilistic model for interrogating the THD for object detection in hyperspectral imagery. In particular, we develop a classifier to identify objects that share similar topological attributes with a training reference object.

Multi-tenancy is used for efficient resource utilization when cloud resources are shared across multiple customers. In cloud applications, the data layer is often the prime candidate for multi-tenancy, and usually comprises a combination of different cloud storage solutions such as relational and non-relational databases, and blob storage. Each of these storage types is different, requiring its own partitioning schemes to ensure tenant isolation and scalability. Current multi-tenant data architectures are implemented mainly through manual coding techniques that tend to be time consuming and error prone. As an alternative, we propose a domain-specific modeling language, CadaML, that provides concepts and notations to model a multi-tenant data architecture in an abstract way. CadaML also provides tools to validate the data architecture and automatically produce application code to implement said architecture.