Richard Blythman, Mohamed Arshath, Salvatore Vivona, Jakub Smékal · 5 authors
AI requires heavy amounts of storage and compute with assets that are commonly stored in AI Hubs. AI Hubs have contributed significantly to the democratization of AI. However, existing implementations are associated with certain benefits and limitations that stem from the underlying infrastructure and governance systems with which they are built. These limitations include high costs, lack of monetization and reward, lack of control and difficulty of reproducibility. In the current work, we explore the potential of decentralized technologies - such as Web3 wallets, peer-to-peer marketplaces, storage and compute, and DAOs - to address some of these issues. We suggest that these infrastructural components can be used in combination in the design and construction of decentralized AI Hubs.
Fabian Kirstein, Anton Altenbernd, Sonja Schimmler, Manfred Hauswirth
The Data Catalogue Vocabulary (DCAT) standard is a popular RDF vocabulary for publishing metadata about data catalogs and a valuable foundation for creating Knowledge Graphs. It has widespread application in the (Linked) Open Data and scientific communities. However, DCAT does not specify a robust mechanism to create and maintain persistent identifiers for the datasets. It relies on Internationalized Resource Identifiers (IRIs), that are not necessarily unique, resolvable and persistent. This impedes findability, citation abilities, and traceability of derived and aggregated data artifacts. As a remedy, we propose a decentralized identifier registry where persistent identifiers are managed by a set of collaborative distributed nodes. Every node gives full access to all identifiers, since an unambiguous state is shared across all nodes. This facilitates a common view on the identifiers without the need for a (virtually) centralized directory. To support this architecture, we propose a data model and network methodology based on a distributed ledger and the W3C recommendation for Decentralized Identifiers (DID). We implemented our approach as a working prototype on a five-peer test network based on Hyperledger Fabric.
Blockchain explorers are important tools for quick look-ups of on-chain activities. However, as centralized data providers, their reliability remains under-studied. As a case study, we investigate Beaconcha.in , a leading explorer serving Ethereum’s proof-of-stake (PoS) update. According to the explorer, we find that more than 75% of slashable Byzantine actions were not slashed. Since Ethereum relies on the “stake-and-slash" mechanism to align incentives, this finding would at its face value cause concern over Ethereum’s security. However, further investigation reveals that all the apparent unslashed incidents were erroneously recorded due to the explorer’s mishandling of consensus edge cases. Besides the usual message of using caution with centralized information providers, our findings also call for attention to improving the monitoring of blockchain systems that support high-value applications.
Alexander Bernauer, Sofia Faro, Rémy Hämmerle, Martin Huschenbett · 11 authors
Distributed ledger technologies, also known as blockchains for enterprises, promise to significantly reduce the high cost of automating multi-party business workflows. We argue that a programming language for writing such on-ledger logic should satisfy three desiderata: (1) Provide concepts to capture the legal rules that govern real-world business workflows. (2) Include simple means for specifying policies for access and authorization. (3) Support the composition of simple workflows into complex ones, even when the simple workflows have already been deployed. We present the open-source smart contract language Daml based on Haskell with strict evaluation. Daml achieves these desiderata by offering novel primitives for representing, accessing, and modifying data on the ledger, which are mimicking the primitives of today's legal systems. Robust access and authorization policies are specified as part of these primitives, and Daml's built-in authorization rules enable delegation, which is key for workflow composability. These properties make Daml well-suited for orchestrating business workflows across multiple, otherwise heterogeneous parties. Daml contracts run (1) on centralized ledgers backed by a database, (2) on distributed deployments with Byzantine fault tolerant consensus, and (3) on top of conventional blockchains, as a second layer via an atomic commit protocol.
The UK has considerably increased the amount of aid it spends on research in recent years.This policy follows statements by DFID that research is among the best ways of spending aid.It also follows the UK legislating a commitment to spend 0.7 percent of GNI as foreign aid.In pursuing this target, the UK has decreased the proportion of bilateral aid spent through DFID and has channelled more through other departments.BEIS and the Department for Health and Social Care have been big recipients, and research accounts for much of their increased share of aid budgets.The information associated with the majority of this research aid is vague, raising questions about transparency.A large amount of the research is financed using an allocation mechanism that effectively ties it to UK institutions.There are also questions as to the poverty focus of some of the research conducted, given the explicit intention of the UK government to find existing activity to reclassify as ODA following the legislating of the 0.7 percent target.We suggest reporting reforms that will increase transparency and allow greater scrutiny of the way UK research aid is spent.We also call for the UK to live up to its reporting to the OECD that all British aid is untied.
Access control is widely used technology for securing sensitive resources of information systems, such as personal data managed by cloud-based data store and sensitive data stream collected by smart devices. Existing access control systems mainly adopt centralized architecture and static access control models, including Access Control List, Role-based Access Control and Attribute-based Access Control. However, these systems fail to meet the increasing requirements of behavior based dynamic access control or requirements of owner initiated autonomous access control without relying on trustworthy third parties and suffer inherent drawbacks of single point of failure or dishonesty. To this end, a novel blockchain-based and provenance enabled dynamic access control scheme called BPDAC is proposed. Specifically, it collects and stores data provenance on blockchain to enable behavior-based dynamic access control; in particular, the quick lookup table structure is designed to speed up access control evaluation based on provenance with increasing complexity. It also provides specifications for formulating access control policies based on provenance. It utilizes a set of smart contracts on blockchain to enable decentralized and reliable autonomous access control. A prototype system is implemented on the Hyperledger Fabric and experiments are conducted to show that the proposed scheme is practically feasible and scalable in terms of the performance metrics of throughput and latency.
The verification of the integrity of data recorded on blockchains is a disregarded challenge. The limitations of blockchains to provide verification mechanisms suggest the necessity to extend the data provenance verification further back into the real-world infrastructure. The deployment of blockchain in critical areas handling sensitive data, such as healthcare machines, motivates the development of such measures. The purpose of this thesis is to explore blockchains in real-world medical applications. The implementation introduces a mechanism to establish a link between real-world medical systems and blockchains by utilizing mechanisms such as Non-Fungible Tokens (NFT), Trusted Platform Module (TPM), and remote attestation. This thesis provides practical insight into the design of blockchain applications with the requirement of digital twin-like properties. In conclusion, TPM and remote authentication mechanisms can be used to verify the integrity of devices and the data they generate. However, the overall architecture of blockchains and the required infrastructure are still lacking. Thus, more investigation is required to understand the actual utility of blockchains.
Blockchain explorers are important tools for quick look-ups of on-chain activities. However, as centralized data providers, their reliability remains under-studied. As a case study, we investigate Beaconcha.in, a leading explorer serving Ethereum's proof-of-stake (PoS) update. According to the explorer, we find that more than 75\% of slashable Byzantine actions were not slashed. Since Ethereum relies on the "stake-and-slash" mechanism to align incentives, this finding would at its face value cause concern over Ethereum's security. However, further investigation reveals that all the apparent unslashed incidents were erroneously recorded due to the explorer's mishandling of consensus edge cases. Besides the usual message of using caution with centralized information providers, our findings also call for attention to improving the monitoring of blockchain systems that support high-value applications.
M. L. Horton, David Chen, Yudan Yi, Xiaohua Wen · 5 authors
<title>Abstract</title> This paper explains some design and architecture decisions around the GEODNET network and the GeoDAO decentralized autonomous organization, which aims to create and operate a truly decentralized public GNSS reference sensing network. This paper covers the motivation of the network, the capabilities of current and future reference stations, the blockchain and GEOD token mechanics, and how the network powers applications ranging from climate change monitoring to real-time centimeter-accurate positioning.
Raul Cristian Bag, Bruno Spilak, Julian Winkel, Wolfgang Karl Härdle
Living in the Information Age, the power of data and correct statistical analysis has never been more prevalent. Academics and practitioners require nowadays an accurate application of quantitative methods. Yet many branches are subject to a crisis of integrity, which is shown in an improper use of statistical models, $p$-hacking, HARKing, or failure to replicate results. We propose the use of a Peer-to-Peer (P2P) ecosystem based on a blockchain network, Quantinar (quantinar.com), to support quantitative analytics knowledge paired with code in the form of Quantlets (quantlet.com) or software snippets. The integration of blockchain technology makes Quantinar a decentralized autonomous organization (DAO) that ensures fully transparent and reproducible scientific research.
BACKGROUND: Collaborative privacy-preserving modeling across several healthcare institutions allows for the construction of more generalizable predictive models while protecting patient privacy. OBJECTIVE: We aim at addressing the site availability issue on a hierarchical network by designing an immutable/transparent/source-verifiable quorum mechanism. METHODS: We developed an approach to combine a hierarchical learning algorithm, a novel Proof-of-Quorum (PoQ) consensus protocol, and a design of blockchain smart contracts. We constructed QuorumChain as an example and evaluated the scenarios of site-unavailability during the initialization and/or iteration phases of the modeling process on three healthcare/genomic datasets. RESULTS: When one or more sites would become unavailable, HierarchicalChain could not function, whereas QuorumChain improved predictive correctness significantly (the full Area Under the receiver operating characteristic Curve, or AUC, improved from 0.068 to 0.441, all with p-values < 0.001). CONCLUSION: By constructing a quorum to continue the modeling process, QuorumChain possesses the capability to tackle the situation of sites being unavailable. It inherits the capability of learning on network-of-networks, improves learning continuity, and provides data/software immutability, transparency, and provenance, which can be important in expediting clinical research.
Elder Bruno Evaristo Correa, Jeffson Celeiro Sousa, Antônio Abelém, Sandro Ronaldo Bezerra Oliveira
In the development of projects that aim at management and editorial evaluation methods, mechanisms that foster the product’s quality final have great importance. In this scenario, several areas are working together in search of better adequacy and standardization in software development. A basic example is the adequations of evaluation of software engineering and computer networks, which work, so that distributed applications are developed following evaluation criteria and standardized quality standards. In this context, we present the DASP software, an open-source distributed autonomous scientific publisher executed through an allowed blockchain network, automatically organized through intelligent contracts, an alternative to the decentralized management of editorial models. As a form of evaluation, one of the most current standards used by the international organization for standardization (ISO) to perform software quality measurements, ISO/IEC 25023, is adopted. Furthermore, we focused on the security aspect, which is one of the categories of ISO/IEC. This aspect was chosen because it was based on the main features that underpin blockchain technology. The quality measurement was carried out following several steps, such as the definition of ISO/IEC 25023, an adaptation of metrics for DASP software evaluation, calculations of the quality value of each functionality, and determination of recommendations for improvements in the software according to the estimates made.
Sarah Wheeler, Darci R Block, Dustin R. Bunch, Jamie Gramz · 7 authors
The American Medical Informatics Association defines biomedical and health informatics as the “science of how to use data, information, and knowledge to improve human health and the delivery of health care services.” More specifically within laboratory medicine, informatics and data analytics use multiple sources of data to improve all aspects of the clinical laboratory, from workflow and personnel to result interpretation. With increasing healthcare information complexity, integration and interoperability issues have become readily apparent between health information systems, bringing to the forefront questions about the validity of data exchange and basic data access. Most data generated within the clinical laboratory are of high quality, well annotated, and structured discretely, however turning these data into useful and actionable information can be a difficult data analytics bridge for many to cross. Instrument and laboratory information system (LIS) vendors are beginning to aid in the creation of generalized reports for common laboratories questions; however, this still falls short of the potential of the clinical laboratory to bring more actionable information to hospital leadership, clinicians, and patients. Collaboration among informaticians, information technology (IT) professionals, and the laboratorians is critical to ensure our health information systems can utilize and report laboratory data clinically, in addition to providing interoperable data streams for furthering research, education, and innovation in healthcare. To discuss these and other challenges and opportunities for informatics in laboratory medicine, we have invited several experts to share their experiences. Darci Block: The COVID-19 pandemic is a case in point for the value of informatics. It certainly was not easy, and there were many lessons learned, but the ability to monitor case rates and predict surges was all thanks to the mighty efforts of clinical laboratorians who became overnight experts of SARS-CoV-2 testing and informatics and data analytics (whether they knew it or not). We also learned that when our collective attention is focused on a single threat, the response can be very targeted and efficient in execution. We accomplished a tremendous amount in a relatively short duration because of this laser focus. Dustin Bunch: At this time, laboratory operation activities are receiving the largest benefits from laboratory data streams in the form of internal and external quality metrics. Most laboratories are monitoring turn-around-times, volumes, QC (quality control), and infection metrics either through reports and/or dashboards, and have mandatory reporting to federal, state, and local entities. David S. McClintock: I thought this would be an easy question; however, it wasn’t—clinical laboratories have benefited from an increased awareness and use of informatics for decades, with incremental changes over time leading to numerous diagnostic, operational, and quality improvements. For example, we have seen improved interfacing and coordination of laboratory instrumentation and automation, most recently in the areas of molecular testing, microbiology, and point-of-care testing. Data-rich analytics are driving laboratory operations more and more, in addition to laboratories seeing minor gains in interoperability with greater adoption of standards such as Logical Observation Identifiers Names and Codes (LOINC) and unique device identification (UDI). J. Mark Tuthill: Clearly, automation of manual processes has had the most direct impact in the clinical laboratory. The impact of business analytics is now having direct impact on the laboratory as well. Because of the use of descriptive analytics, predictive analytics, and artificial intelligence, laboratories have a much deeper understanding of their workflow and any deviations and thus can respond in ways not previously available. I believe this will continue to develop in the future into next-generation automation and laboratory efficiency. Edward Ki Yun Leung: The areas for which I have seen the most improvement by the increased use of informatics are in the core or integrated laboratory environments with total laboratory automation. Vendors are providing tools, usually at a middleware level, where data from the instruments can be analyzed and presented on dashboards for laboratory staff and management. The dashboards can be successfully used in different ways such as monitoring turnaround time, identifying bottlenecks in laboratory operation workflows, optimizing staffing resources to support changes in testing volumes throughout the day, and supporting test utilization programs. Jamie Gramz: Standardization, improved efficiency, and reduction of hands-on time are key improvements made possible with lab informatics. Informatics has accelerated the laboratory’s ability to generate, aggregate, and analyze data and has been the key enabler in operationalizing data through use of automation, while also helping labs to offset the growing shortage of laboratory professionals in the US and other countries around the world. Whether it be the transformation of tests ordered by a physician into the autonomous handling of samples throughout the preanalytic, analytic, and postanalytic processes or the use of autoverification to streamline the evaluation, review, and reporting of patient results, informatics is helping to drive the timely delivery of actionable patient information that medical laboratories provide. J. Mark Tuthill: There are multiple factors that impact the quality and interpretability of laboratory data by clinicians and patients. First, the ability to see, understand, and read these data in an easy fashion has been challenging. Most laboratory reports are very flat, textual, and are not summative. Nor is there interpretive guidance provided: “You got the number, figure it out.” This is not helpful. We need better graphical displays of laboratory data for patients and clinicians. In addition, because laboratory results produced by different laboratories may have different reference intervals or completely different result values, it can be very difficult for clinicians and patients to interpret results across different healthcare systems. So providing interoperable, interpretable results is only part of the challenge. Dustin Bunch: Foremost is data access, which is the most common barrier for those that want to do data science. As a community, we have not created a culture where it is normal to routinely access raw data. Historically, if data were available, access to those data came through distilled reports. Another issue is the number of people trained to process and/or interpret the data available in clinical laboratories. Edward Ki Yun Leung: Some of the current operational challenges we face that impact high-quality, interpretable laboratory data include decentralized databases and the need of multiple different data mining tools to extract the data. Both may have significant impact on the fidelity of the final data. In the current healthcare environment, data are stored in multiple systems such as electronic health records (EHR), laboratory information systems, clinical decision support systems, clinical operations and analytics software and systems, revenue cycle management systems, and software systems that support clinical trials and research. Each of these systems may have different levels of accuracy and refresh rates. In addition, each system may require a different data mining tool to extract the data. High skill-set requirements may be needed to effectively use these tools to extract, combine, and format the data. Jamie Gramz: Interoperability has come a long way over the past 10 years with completion of the 3 stages of meaningful use: driving EMR (electronic medical record) integration including data capture and sharing (2012), advanced clinical processes (2014), and improved outcomes (2016). And as a patient, I appreciate being able to quickly see my lab results in the patient portal app as soon as they are released from the laboratory. Providing patients with immediate and transparent access to their health information was an important achievement and great step forward. But in today’s consumer-driven society empowered by the internet, it introduces a new set of challenges as patients try to understand and interpret the meaning of their lab results, potentially even attempting to self-diagnose medical conditions. This may create new opportunities for laboratories to provide support for lab test result interpretation and expanding patient portal apps to include access to relevant and accurate information as a logical next step. Darci Block: Clinicians and patients seem most interested in having all “relevant” health record information in one place that is easy to reference and digest quickly. It makes shopping for healthcare more feasible and streamlines the experience when a mountain of paper results and records do not need to be synthesized at each stop. To that end, it seems like a simple thing to pull laboratory results into a single system or viewer from any place a patient has lab testing performed to support this endeavor. However, operationally the methods of standardizing results (via LOINC and other standards) have not completely overcome the challenge for reasons I am not altogether sure of. Additionally, results nomenclature (e.g., positive = “P,” “+,” “detected,” “reactive,” “confirmed,” “present”) remains a ripe opportunity for standardization to consolidate meaning in such collections of results. David S. McClintock: I like to think of laboratory informatics as how we best deliver the right clinical laboratory information to the right person, at the right place, at the right time, and in the right way. With that in mind, clinical laboratories are still far behind in delivering the “right” laboratory information to the right person. We still provide a single result or interpretation in a one-size-fits-all approach, with each lab formatting their results in different ways that can confuse both patients and clinicians alike. Unfortunately, our current lab information systems do not allow us to send multiple versions of results for multiple purposes (although, to be fair, downstream HIS [health information systems] can’t ingest differing versions of the same result either), which means it will be a long time before we can tailor our reports to meet the specific needs of the customer/right person (e.g., patient, primary care physician, subspecialty clinician, etc.). Jamie Gramz: A common one is the slow adoption of analytic solutions to help monitor performance, with many labs still following tedious and time-consuming steps to collect the data needed to manually generate reports. Automating this process with informatics solutions that provide real-time analytic reports to monitor the common key performance indicators that most labs measure could be a “low-hanging fruit” opportunity to help drive continuous improvement. Real-time analytics solutions can make it easier for labs to assess performance, identify inefficiencies, and drill-down to determine the root causes of problems. Whether it be to monitor internal metrics like turnaround time, throughput, and exception management or to investigate complex issues like identifying the leading sources of sample integrity issues, analytics should be a key tool used by the laboratory to help make data-driven decisions for improvement. J. Mark Tuthill: The biggest area where we are lagging in our use of data is widespread access to all varieties of data and the ease of access to that information. Once data is available, the ability to display that data in meaningful ways, to the correct people, at the correct time, is the next challenge. Typically, the laboratory has relied on paper data outputs to respond to workflow challenges or defects/deviations in the laboratory testing process. This is true in both preanalytic, analytic, and postanalytic processes. Replacing static, paper-based reports with dynamic, real-time dashboards is still in its infancy in many laboratories, particularly for real-time dashboards that would have direct day-to-day impact on workflow and patient care activities beyond simple “turnaround times.” Darci Block: In my experience, I would say all areas of laboratory medicine lag because access to data, even the most basic data within the laboratory information system, not to mention instrument and middleware data, is limited; these systems were designed to drive and of in time and not designed to be and for also very because we the information is there but of Edward Ki Yun Leung: area where we are lagging in our use of data to drive improvements is in point-of-care testing is very different when to laboratory testing. vendors may have their software and/or system for their and a may have to 3 even different It is not for a to use a middleware to the different software and/or systems to the and/or to the clinical laboratories, there are not as many tools to extract, and the data. Another area in where we are lagging in our use of data is in testing personnel for where there can be more For each we need to the education, and This can be very and because the information may be in paper format and in multiple Dustin Bunch: The as the value clinical laboratories are lagging in all areas when it to of data science. The lab to be in the only descriptive analytics and analytics it To data analytics in the clinical laboratory, we need to into the which more with predictive will and analytics can we make David S. McClintock: labs are well with descriptive analytics in most are not to to analytics, such as analytics predictive analytics will in the lab and and analytics can we make and in the In more value from data to increasing resources and analytics integration of operational and data, including ways to both real-time awareness of and to on and analytics analytics, integration of data with increasing analytics efforts and which been a for clinical laboratories, and laboratory medicine and healthcare systems alike. David S. McClintock: we need to more in informatics people, and laboratories, and their laboratory informatics or their and middleware clinical business Additionally, clinical laboratories need to their awareness of informatics and both in their and leadership, in addition to any informatics clinicians and needs to with their information technology and clinical informatics to ensure they have a at the the labs to both understand immediate issues at and to the about and informatics Edward Ki Yun Leung: A we face in the use of informatics is support and laboratory staff may have knowledge and experience with and may have knowledge and experience in laboratory Both will be needed for the of informatics that will be useful in laboratory We will need hospital and laboratory to and the resources to support this Darci Block: To improve need to have a basic understanding of are how it and it to should and is it do and how one In my as of I have into a as of and other resources for our The is with the and of which and from and efficient and processes for the right at different while quality and I as a to a need or from the lab to that are for and/or systems to meet both I think we can overcome by within an but also between to and best and lessons learned from one Jamie Gramz: is available and where to is a common challenge. With many solutions designed to a of lab it can be difficult to determine which will best in the determine which potential improvements will have the most significant impact to the laboratory and the there to patient need to issues, such as result reporting or that help to the use of lab staff or laboratory be operations are in would real-time analytics be the next step to improve Dustin Bunch: The future of data in the clinical laboratory will have to from a single and to data from and hospital data There are many to but there have been made to make this with like common data such as the Medical from health data and better and J. Mark Tuthill: I believe the biggest the laboratory in informatics is to the of human resources with data and personnel with experience in business analytics as well as staff who the value of analytics to drive workflow we are by of time and needs to be improved is the for analytics and of clinical laboratory as well as and who support operations across the laboratory new for the clinical laboratory is the use of that measure (e.g., and laboratory testing in are that will require new informatics As these are in their we have much experience these data be integrated to the electronic medical the other of how long should data be stored and is quality accomplished with such I am questions more these may future Jamie Gramz: Most in vendors provide solutions to help patient and quality testing, but that can value in helping the lab overcome key management solutions can the and help the process. an management can help and or to lab staff to time more meaningful monitoring and solutions can and of and automation systems in multiple laboratories from a single and reporting solutions can make it easier to monitor performance, identify inefficiencies, and investigate root causes of problems. J. Mark Tuthill: to business analytics, there are vendors that will provide analytic solutions that to the laboratory information system and help support laboratories in these business processes. However, these tools on data that is readily available to these Typically, vendors do not have access to the laboratory information systems and do not have knowledge of the laboratory information systems or its they on the laboratory to provide personnel to with of these of personnel the Once analytics tools, and are they can be by the laboratory with ease and The vendors of instruments and automation are also to make such tools basic in the at it is a process of may can also be to help laboratories understand they may Darci Block: It would be great if vendors could efforts to between common systems and instruments as well as systems and great would it be if of like the could from the experience of who have this and share experience to for David S. McClintock: vendors can by tools for labs to access their data, both for internal and for to is also vendors can better meet complex laboratory informatics to and by current ways vendors can help laboratories reporting tools to allow for easier and interoperability with other basic descriptive analytics and tools within the to support lab better integration of of within their to support for and including on labs can create or apps that in to software and create new many Edward Ki Yun Leung: such as the American Association for the American for and the Association for Informatics are great resources for laboratory professionals and to about each there are and by each of these vendors can provide support and resources to these and with these vendors are providing tools that well within their however, laboratories use one for their test Vendors can help by tools that make it easier to and use the data between different systems and Dustin Bunch: Vendors could help by their software to data into their and make it easy to This may require the software to be able to with current data such as and or to to Edward Ki Yun Leung: We can best improve the clinical laboratory and the of laboratory medicine by expanding and informatics to staff at the laboratory Informatics is to laboratory Once staff are more and with informatics can be integrated into laboratory operations and their will be able to experience the benefits of provide on the tools, and to the future of the We will be able to the right information, to the right person, at the right Dustin Bunch: The lab will be better able to if we and data into our We should be able to predict instrument issues before that create to In addition, data can help improve laboratory efficiency, but this is on many of data in the lab is to data The number and of tests are which the of interpretation. the laboratory is able to this would be a for the clinicians and patients. This can also be to for patients to understand their information J. Mark Tuthill: There are several areas that we need to and in how we can best improve the laboratory informatics. and are workflow processes. informatics tools to and understand workflow and laboratory by is step Once workflow tools have been into place, laboratory information technology needs to be in an This will have direct operational impact on laboratory efficiency, and of the laboratory information system is key and remains key to laboratory testing as well as the of Once workflow has been improved and laboratory information system technology is in place, we can to use analytics and to not only assess our workflow activities and our laboratory but to these tools in For example, artificial can be used to not only help understand laboratory testing but to help understand in laboratory testing that in outcomes or of are in their past business analytics into clinical analytics is the ability to use clinical information in actionable ways that impact patient this is the next of that laboratories can This will tools such as that are able to create laboratory and on that this information from simple testing results. David S. McClintock: The future of clinical laboratory informatics in access to all laboratory data, not and that data into data that specific operational and clinical and that data with artificial both within and external to the laboratory, to operational workflows, manual and to drive for patients and provide on clinical A better understanding and improved integration is also important clinical laboratories can with to new and improve As more to the labs will have to to how instrumentation are how they access and data, and how they their and other lab Jamie Gramz: laboratory testing has over the past years to become and there has not been much in the area of clinical decision support There remains a tremendous amount of human in the process of and lab are with not only the tests for a patient but also lab test results. to provide support for lab test result test and predictive to help the identification of patients at of specific are where could Informatics solutions for and the increased use of and artificial will be key to help the laboratory the value it to beyond the reporting of test results and reference Darci Block: To see the it will a that but also makes and decisions to that will outcomes and business that are of systems and processes that can provide and that when make the best use of systems that data is and in its most meaningful that identify and issues and challenges as they and provide into and And clinical laboratories that the data be of this to have any of human health and the delivery of healthcare Interoperability on Medical on Interoperability they have to the of this paper and have the following significant to the and of data, or and interpretation of or the for final of the and to be for all aspects of the thus that questions to the accuracy or integrity of any part of the are and all the and/or potential of J. Association for of in the of and of or J. of and support for and/or for in in the presented at Informatics on and same for at on support for and/or for and support for and/or for
<ns4:p>Scientists write research articles, process ethics reviews, evaluate proposals and research, and seek funding. Several strategies have been proposed to optimize these operations and to decentralize access to research resources and opportunities. For instance, we previously proposed the trinity review method, combining registered reports with financing and research ethics assessments. However, previously proposed systems have a number of shortcomings, including how to implement them, e.g., who manages them, how incentives for reviewers are paid, etc. Various solutions have been proposed to address these issues, employing methods based on blockchain technologies, called “decentralized science (DeSci)”. Decentralized approaches that exploit these developments offer potentially profound improvements to the troubled scientific ecosystem. Here, we propose a system that integrates ethics reviews, peer reviews, and funding in a decentralized manner, based on Web3 technology. This new method, named ABCDEF publishing, would enhance the speed, fairness, and transparency of scientific research and publishing.</ns4:p>
A knowledge market can be described as a type of market where there is a consistent supply of data to satisfy the demand for information and is responsible for the mapping of potential problem solvers with the entities which need these solutions. It is possible to define them as value-exchange systems in which the dynamic features of the creation and exchange of intellectual assets serve as the fundamental drivers of the frequency, nature, and outcomes of interactions among various stakeholders. Furthermore, the provision of financial backing for research is an essential component in the process of developing a knowledge market that is capable of enduring over time, and it is also an essential driver of the progression of scientific investigation. This paper underlines flaws associated with the conventional knowledge-based market, including but not limited to excessive financing concentration, ineffective information exchange, a lack of security, mapping of entities, etc. The authors present a decentralized framework for the knowledge marketplace incorporating technologies such as blockchain, active inference, zero-knowledge proof, etc. The proposed decentralized framework provides not only an efficient mapping mechanism to map entities in the marketplace but also a more secure and controlled way to share knowledge and services among various stakeholders.
Élton Carneiro Marinho, Éber Assis Schmitz, Sérgio Manuel Serra da Cruz
Blockchain technology combined with Data provenance is one way to make soil data more trustworthy and traceable by providing tamper-proof information about the origin, transformations, and history of pieces of data. We present Hyperledger Fabric of FAIRCHAIN, a computational infrastructure that manages smart contracts that uses soil data. We aim to mitigate the open challenges of the agricultural food supply chain, specifically in the difficulty of traceability of soil data. In this work, we present the mechanism to structure a smart contract using soil data enriched with retrospective provenance metadata. The infrastructure can hold workflow implementations.
Open access
Scientific Computing and Data Management
Blockchain Technology Applications and Security
Innovative Microfluidic and Catalytic Techniques Innovation
Luciano Baresi, Giovanni Quattrocchi, Damian A. Tamburri, Luca Terracciano
The deployment and management of Blockchain applications require non-trivial efforts given the unique characteristics of their infrastructure (i.e., immutability) and the complexity of the software systems being executed. The operation of Blockchain applications is still based on ad-hoc solutions that are error-prone, difficult to maintain and evolve, and do not manage their interactions with other infrastructures (e.g., a Cloud backend). This paper proposes KATENA, a framework for the deployment and management of Blockchain applications. In particular, it focuses on applications that are compatible with Ethereum, a popular general-purpose Blockchain technology. KATENA provides i) a metamodel for defining Blockchain applications, ii) a set of processes to automate the deployment and management of defined models, and iii) an implementation of the approach based on TOSCA, a standard language for Infrastructure-as-Code, and xOpera, a TOSCA-compatible orchestrator. To evaluate the approach, we applied KATENA to model and deploy three real-world Blockchain applications, and showed that our solution reduces the amount of code required for their operations up to $82.7\%$.
Nikos Kefalakis, Aikaterini Roukounaki, John Soldatos, Mauro Isaja
This chapter presents a dynamic and programmable distributed data analytics solution for industrial environments. The solution includes an edge analytics engine for analytics close to the field and in line with the edge computing paradigm. Each edge analytics engine instance is flexible and dynamically configurable based on an Analytics Manifest (AM). It is also based on distributed ledger technologies for configuring analytics tasks that span multiple edge nodes and instances of the edge analytics engine. In particular, it leverages ledger services for synchronizing and combining various AMs in factory wide analytics tasks. Based on these mechanisms, the presented distributed data analytics infrastructure is therefore flexible, configurable, dynamic and resilient. Moreover, it is open source and provides Open APIs (Application Programming Interfaces) that enable access to its functionalities. These features make it unique and valuable for vendors and integrators of industrial automation solutions.
In this paper, we introduce SwarMED, a decentralized yet high throughput interoperability system for big biomedical data. SwarMED uses Etehreum blockchain for trustless security and Swarm p2p storage to handle high throughput transaction of big data. In SwarMED, we developed an indexing mechanism over the immutable storage of Swarm to achieve high-throughput while sharing millions of patient records and images among multiple parties. SwarMED achieved a high throughput of 250K medical records per second over a private network constructed over LSU-HPC cluster. This high throughput is 9x more comparing to conventional way of using p2p storage in conjunction with blockchain. This high throughput enables the patients to get realtime access to his comprehensive medical history and scientists to gain real-time access to different medical data for collaborative research complying to the constraints posed by existing laws. Our system-level analysis over different design alternatives over different transfer and storage architectures shows that, p2p storage platforms automatically provide significantly better scalability over traditional HTTP with increasing number of clients. Swarm provides 2x more I/O throughput and 10x less latency than IPFS, another p2p storage system making it a better choice for decentralized big data transaction.
The introduction of electronic personal health records (EHR) enables nationwide information exchange and curation among different health care systems. However, the current EHR systems do not provide transparent means for diagnosis support, medical research or can utilize the omnipresent data produced by the personal medical devices. Besides, the EHR systems are centrally orchestrated, which could potentially lead to a single point of failure. Therefore, in this article, we explore novel approaches for decentralizing machine learning over distributed ledgers to create intelligent EHR systems that can utilize information from personal medical devices for improved knowledge extraction. Consequently, we proposed and evaluated a conceptual EHR to enable anonymous predictive analysis across multiple medical institutions. The evaluation results indicate that the decentralized EHR can be deployed over the computing continuum with reduced machine learning time of up to 60% and consensus latency of below 8 seconds.