Cloud Data Acquisition from Shared-Use Facilities in A University-Scale Laboratory Information Management System

SummaryThis paper deals with data transfer between two meshes as it happens in a finite element context when a remeshing has to be performed. We propose a finite‐volume‐based data transfer method for an efficient remeshing of three‐dimensional solid mechanics problems. The originality of this transfer method stems from a linear reconstruction of the fields to be transferred on an auxiliary finite volume mesh, a fast computation of the transfer operator and the application to the complete remeshing of 3D problems. This procedure is applicable to both nodal values and discrete fields defined at quadrature points. In addition, a data transfer method using mortar elements is presented. The main improvement made to this second method comes from a fast computation of mortar elements. These two data transfer methods are compared with the simplest transfer method, which consists of a classical interpolation. After some academic examples, we present 2D forging and 3D friction stir welding applications. Copyright © 2014 John Wiley & Sons, Ltd.

European start-up companies must overcome more ‘transfer hurdles’ when personal data is transferred from the European Union to the US (United States of America) as part of crowdfunding campaign activities. Transfer of personal data is commonly not associated with (small scale) crowdfunding activities. However, the strict rules of the EU GDPR (European General Data Protection Regulation) on safeguarding personal data apply to all companies when data is transferred from the EU to the US - regardless the size of the business.
 This article identifies exchange of personal data that takes place between primarily fundraiser and crowdfunding service provider in different steps of fundraising campaigns. The framework for rewardbased crowdfunding for goods production that is provided by the US based Indiegogo platform is used as example and context. The article highlights by way of example the obligations that must be met by European fundraisers as "data controllers" when personal data is transferred to Indiegogo. No easy solutions are provided by either European Union or national data protection authorities on how to establish an adequate level of personal data protection. Paradigms on how to secure transfer of personal data to third countries are available in form of so-called standard contractual clauses, but still conditions for transfer of personal data from Europe to the US are hard to comply with. Apart from entering into an inter partes agreement on use of standard contractual clauses with the crowdfunding platform provider, a European fundraiser must furthermore make a so-called "transfer impact assessment" to ensure that third party access to personal data is avoided. In the case of transfer of personal data from the EU to the US the fundraiser must consider using encryption of data as a "supplementary measure" to block third party access. Encryption of data is however not suitable for exchange of data in a dynamic crowdfunding campaign so other means for protection of data must be found and applied.
 The reason and explanation for making data transfers from the EU to the US that hard for e.g., fundraisers are thus to be found at interstate level in the relation between the EU and the US. According to EU law, more specifically the GDPR and several of the provision of the Charter of Fundamental Rights of the European Union, US security legislation authorises a disproportionate access for US intelligence services to citizens' personal data. A solution on manageable transfer of personal data from the EU to the US may be found before the end of 2022, since a new TADP (Trans-Atlantic Data Privacy Framework) is currently being negotiated between EU and US at top politician level. However, the implementation of the TADP may take som time since the EU legislative framework needs adjustments to make the new transfer possibilities operational.

In designing query processing primitives, a crucial design choice is the method for data transfer between two operators in a query plan. As we were considering this critical design mechanism for an in-memory database system that we are building, we quickly realized that (surprisingly) there isn't a clear definition of this concept. Papers are full of ad hoc use of terms like pipelining and blocking, but these terms are not crisply defined, making it hard to fully understand the results attributed to these concepts. To address this limitation, we introduce a clear terminology for how to think about data transfer between operators in a query pipeline. We argue that there isn't a clear definition of pipelining and blocking, and that there is a full spectrum of techniques based on a simple concept called unit-of-transfer. Next, we develop an analytical model for inter-operator communication, and highlight the key parameters that impact performance (for in-memory database settings). Armed with this model, we then apply it to the system we are designing and highlight the insights that we gathered from this exercise. We find that the gap between the traditional “pipelining” and “non-pipelining” methods of query processing, w.r.t. key factors such as performance and memory footprint is quite narrow, and thus system designers should likely rethink the notion of “pipelining” vs. “blocking” for in-memory database systems.

Health and lifestyle wearables, such as smartwatches and fitness trackers, are popular consumer choices for monitoring their health status. Singapore has a growing market for smartwatches, with Apple and FitBit being popular choices amongst consumers. These wearables generate vast amounts of data which are often transmitted for processing and storage in the clouds or servers located internationally. The terms of data collection, transfer and use are generally formulated by commercial technology companies that market these devices. This approach could likely result in privacy harm to users such as the unauthorised disclosure of their personal information. This paper draws on Singapore as a case study in examining international data transfer using document analysis of the most popular smartwatches and fitness trackers used by Singaporean consumers. It aims to assess their compliance with Singapore's data protection standards and identify distinct limitations and any inconsistencies that exist in these terms. The proposed recommendations are designed to help technology companies improve their privacy policies pertaining to data transfer while clarifying the concept of data transfer through a comparison of how the terminology is defined in different jurisdictions. Greater clarity is likely to improve accountability in how user data are transferred across jurisdictions.

Cloud computing brings a number of compliance risks to organisations because physical perimeters are not clearly delimited. Many regulations relate to the location of the data processing (and storage), including the EU Data protection directive. A major problem for cloud service consumers, acting as data controllers, is how to demonstrate compliance to data transfer constraints. We address the lack of tools to support accountable data localization and transfer across cloud software, platform and infrastructure services, usually run by data processors. In this paper we design a framework for automating the collection of evidence that obligations with respect to personal data handling are being carried out in what concerns personal data transfers. We experiment our approach in the Open Stack open source IaaS implementation, showing how auditors can verify whether data transfers were compliant.

Optical packet-switching (OPS) networks remain an emerging technology, as exemplified by the feasibility challenges of optical RAM. Nevertheless, OPS networks possess significant potential advantages, including a high data transfer rate and low power consumption due to the elimination of optical–electrical–optical conversion, compared to conventional electronic packet-switching networks, flexibility in handling traffic fluctuations due to packet-switching principles, and low latency as they do not require overhead for communication path establishment. However, effective data recovery from non-negligible packet loss due to optical packet collisions remains a critical challenge in OPS networks. Packet-level forward error correction (FEC) is regarded as an effective data recovery method for lossy networks, such as OPS ones, because it does not rely on packet-by-packet feedback, which could be lost, and avoids the delays associated with feedback and retransmission. On the other hand, FEC requires the transmission of additional packets beyond the original data, increasing the computational load associated with encoding and decoding. In this paper, we propose a data transfer method based on erasure coding, which is useful not only in OPS networks but also in other network environments suffering from severe packet loss due to collisions or signal degradation, such as optical circuit switching networks and wireless networks. This method improves effective data transfer throughput by limiting the number of transmitted packets and reducing the computational overhead of the encoding and decoding processes. This improvement is achieved through the appropriate configuration of the distribution of XOR operation target packets that constitute XOR packets, thereby restricting the number of symbols encoded in each XOR operation and introducing cumulative feedback of which symbols were already recovered at the receiver. To validate the proposed method, we conducted not only simulation evaluations but also what we believe to be the world’s first packet transmission demonstration experiment using packet-level erasure coding over an actual OPS network testbed, taking actual computational overhead into account. The experimental results show that, in high transmission rate environments, the proposed method achieves up to a 188.7% increase in effective data transfer throughput compared to the benchmark LT-Codes-based packet-level FEC transmission method while reducing the processing time on the sender side by up to 15.6% and on the receiver side by up to 39.4%. In addition, the number of packets required for data decoding can be reduced by up to 63.3%.

This study focuses on the data protection of Indonesian citizens in transborder personal data transfers between Indonesia and Japan, which is increasingly relevant in the era of global connectivity and digital advances. Concerns about protection of personal data and security of personal data is increasing following existing progress. This research uses a comparative juridical approach, which allows for a comparison between the legal and regulatory frameworks protecting personal data in the two countries and conducts a comparative study of the two principles. The findings from this study show that Indonesia and Japan have implemented laws to protect the personal data of their citizens. Although there are similarities in the protection of personal data, there are differences in the mechanisms governing the requirements for transborder transfers of personal data between the two countries. This study uses a descriptive-analytic method to provide an overview and analysis of the application of regulations based on applicable legal provisions. The main objective is to analyze the differences and similarities between the two laws to draw general conclusions regarding the approaches and rules applied to the protection of personal data in Indonesia and Japan. Thus, this research provides important insights into the safeguarding of personal data in the context of transborder data transfers between the two country.This study focuses on the data protection of Indonesian citizens in transborder personal data transfers between Indonesia and Japan, which is increasingly relevant in the era of global connectivity and digital advances. Concerns about protection of personal data and security of personal data is increasing following existing progress. This research uses a comparative juridical approach, which allows for a comparison between the legal and regulatory frameworks protecting personal data in the two countries and conducts a comparative study of the two principles. The findings from this study show that Indonesia and Japan have implemented laws to protect the personal data of their citizens. Although there are similarities in the protection of personal data, there are differences in the mechanisms governing the requirements for transborder transfers of personal data between the two countries. This study uses a descriptive-analytic method to provide an overview and analysis of the application of regulations based on applicable legal provisions. The main objective is to analyze the differences and similarities between the two laws to draw general conclusions regarding the approaches and rules applied to the protection of personal data in Indonesia and Japan. Thus, this research provides important insights into the safeguarding of personal data in the context of transborder data transfers between the two country.

This paper firstly discusses the challenges of big data platform for scientific research in the abilities of data storage, processing, transferring and sharing based on the example of data processing in the field of high energy physics. Then the paper presents a typical structure of big data platform for scientific research and gives a brief review on some key technologies, including data acquisition, cleaning, storage, analysis, transfer, sharing and security. The popular systems and the current research situation of these key technologies are also described in the paper. Finally, the concept and framework of the big data platform of High Energy Physics Institute of Chinese Academy of Sciences is introduced in this paper. This open platform is established as the solution of challenges/problems in big data evolution, for example, data acquisition, data transferring, and application interface.

Evaluating new ideas for job scheduling or data transfer algorithms in large-scale grid systems is known to be notoriously challenging. Existing grid simulators expect to receive a realistic workload as an input. Such input is difficult to provide in absence of an in-depth study of representative grid workloads. In this work, we analyze the ATLAS workload processed on the resources of NDG Facility. ATLAS is one of the biggest grid technology users, with extreme demands for CPU power and bandwidth. The analysis is based on the data sample with ~1.6 million jobs, 1,723 TB of data transfer, and 873 years of processor time. Our additional contributions are (a) scalable workload models that can be used to generate a synthetic workload for a given number of jobs, (b) an open-source workload generator software integrated with existing grid simulators, and (c) suggestions for grid system designers based on the insights of data analysis.

Evaluating new ideas for job scheduling or data transfer algorithms in large-scale grid systems is known to be notoriously challenging. Existing grid simulators expect to receive a realistic workload as an input. Such input is difficult to provide in absence of an in-depth study of representative grid workloads. In this work, we analyze the ATLAS workload processed on the resources of NDG Facility. ATLAS is one of the biggest grid technology users, with extreme demands for CPU power and bandwidth. The analysis is based on the data sample with ~1.6 million jobs, 1,723 TB of data transfer, and 873 years of processor time. Our additional contributions are (a) scalable workload models that can be used to generate a synthetic workload for a given number of jobs, (b) an open-source workload generator software integrated with existing grid simulators, and (c) suggestions for grid system designers based on the insights of data analysis.

e23017 Background: Electronic data capture (EDC) has the potential to improve data accuracy and efficiency. As an initial step toward improvement in the accuracy and completeness of data acquisition for clinical trials, PEP-CTN and PBTC conducted a pilot project to assess the feasibility of automated transfer of laboratory data from participating institutions’ electronic health records to the NCI Medidata Rave EDC that is housed within each consortium’s Uniform Resource Locator (URL). The pilot aimed to assess feasibility of automated laboratory data extraction and transfer and to evaluate discrepancies between laboratory data obtained retrospectively through automated extraction and data manually ascertained during the original conduct of the study. Methods: Data from patients treated on 7 completed PEP-CTN or PBTC trials at 7 sites were included. Each consortium created a Rave EDC study to receive data. Sites used the ExtractEHR R package or local methods to map and extract laboratory data for designated patients. Post-extraction data were uploaded via a file transfer protocol (FTP) periodically polled by the Rave Batch Uploader (BU) that uploaded data into Rave EDC. After successful upload, extracted data (EHR) were compared to laboratory data that had been manually reported when patients were enrolled on the trials (MANUAL) and discrepancies were identified. Discrepancy types were categorized and 2 sites reviewed discrepancies at their sites to identify reasons. Results: Preparation and support for automated extraction and upload required significant central operations and local site effort. All sites successfully extracted specified laboratory data and had at least one complete upload via BU to Rave EDC. There were more laboratory data in the EHR datasets than MANUAL datasets across most sites. These results were due to multiple EHR results on a given day or from additional days in the study window than specified in MANUAL. Sites verified that laboratory data in MANUAL and not EHR were stored in un-extractable formats (e.g. PDFs) or outside laboratories. Most discrepant results in both EHR and MANUAL were mismatched values or units. Conclusions: This pilot demonstrated the feasibility of implementing automated laboratory data extraction and transfer in the cooperative group setting. Automated data transfer requires significant central operational support as well as detailed protocols, data specifications, and case report forms and engagement of site technical teams to be successful, but also permits collection of more comprehensive data with reduced manual data entry. Clinical trial information: NCT05020951 .

Darkness, Datafication, and Provenance as an Illuminating Methodology

Prompt and reliable communication between vehicular nodes are essential as its limited coverage and dynamic mobility rate introduces frequent change of network topology. The key feature of vehicular communication that establishes direct connectivity or Road Side Unit-based data transfer among vehicular nodes is responsible for sharing emergency information during critical situations. Multicast routing data dissemination among vehicular nodes is considered to be the potential method of parallel data transfer as they facilitate the option of determining an optimal multicast tree from feasible number of multicast trees established between the source and destinations. This estimation of optimal multicast tree using meta-heuristic techniques is confirmed to improve the throughput and reliability of the network when QoS-based constraints are imposed during multicast routing. An Improved Shuffled Frog-Leaping Algorithm-Based QoS Constrained Multicast Routing (ISFLABMR) is proposed for estimating an optimal multicast tree that confirms effective multi-constrained applied multicast routing between vehicular nodes. ISFLABMR minimizes the cost of transmission to 22% by reducing the number of multicast clusters formed during multicasting through the utilization of local and global-based optimizations. The simulation results of ISFLABMR proveits predominant reduction rate of 24% and 21% in average packet latency and energy consumptions incurred under multicast routing.

In this digital era, there is a trend of international business expansion by conducting corporate actions, including acquisition, which causes the transfer of personal data both domestically and cross-border due to post-acquisition integration. Meanwhile, in Law No. 27 of 2022 on Personal Data Protection, there are only separate provisions in Article 48 and Article 56 regarding corporate actions and cross-border transfers of personal data, without regulating the correlation between these two provisions. This could potentially lead to the automatic transfer of personal data outside Indonesian jurisdiction as a result of post-acquisition integration. This paper aims to analyse the urgency of establishing a post-acquisition cross-border personal data transfer regulation and formulate an ideal regulation to address this legal issue. The research method used is normative juridical research with a statutory, comparative, conceptual, and case approach, with legal material collection techniques by literature and website study, and analysed using systematic, grammatical, and comparative interpretations. From the results of this research, the author found that there are norm incompleteness in this law. Therefore, the Indonesian government can adopt the provisions in the European Union The General Data Protection Regulation 2016/679 in formulating the ideal provisions regarding the cross-border transfer of personal data post-acquisition.

Cross-border data flows management and privacy protection are placed high in the international digital agenda due to unprecedented growth in the volume and pace of data collection, processing, storage and transfer globally. Despite the high importance of data flows regulation and its serious influence on all enterprises involved in digital economy, there is little research conducted in Russia and systemizing the national strategies in this sphere of regulation. The article provides an overview of the existing approaches of different countries to data protection, transfer (cross-border included) and storage, analyses the impact of regulation on international trade flows, and develops proposals for possible measures to reduce costs for companies in the digital age. The research discovers that today most countries of the world regulate personal data and other categories of sensitive data flows through the introduction of either a separate law or data protection provisions in the relevant sectoral laws. The countries’ approaches range from a complete ban on the cross-border transfer of all or certain categories of data to foreign countries to complete liberalization in this area. The most common approach is the introduction of one or several restrictions from the set of measures related to cross-border data transfers: data localization requirement; limitations on the number or type of countries to which sensitive data can be transferred without additional requirements; and the requirement of the personal data subject’s consent or responsible public authorities’ permission.