Archiving Propulsion Laboratory Data

Access control is a key problem for information processing, especially in a distributed environment such as the Internet and intranet, where a large amount of diverse information resources within an enterprise will be made available for groups of diverse users to query. Information documents such as technology secrets and personal records are sensitive and should be accessible to a select group of users based on their position in a company or an organization or even based on how much the user is paying to maintain his/her right for information access. The access control problem, informally, is to determine which user is allowed to access what information. Access control for Internet information processing, in contrast to access control in a traditional operating system, has higher demand in dealing with a much larger scale problem in real time, due to the large amount of information and number of users in the Internet/intranet environment. We present an efficient method for the access control problem in which there are a large number of users and access groups. The main ingredient of our method is a representation of a hierarchical access group structure in terms of intervals over a set of integers and a decomposition scheme that reduces any group structure to ones that have interval representations. The interval representation allows the problem for checking access rights to be reduced to an interval containment problem. We use the interval tree, a popular data structure in computational geometry, to efficiently execute the access-right checking method.

Key TakeawaysFour pilot teams shared experiences with piloting during the COVID‐19 pandemic, emphasizing that thoughtful planning streamlines the piloting process while mitigating the risk of transmitting illness.Remote access to and control of pilot equipment, along with the use of virtual communication platforms, enabled teams to successfully perform pilot‐scale studies.Operating pilot systems through the pandemic allowed teams to respond to drastic changes on a small scale to mitigate risks around full‐scale processes at the treatment plant.

Before the implementation of product data management (PDM) system, person model and enterprise process model (EPM) must be firstly established. For the convenience of project management, all the related users must be allocated to the “Person-User-Role-Group“ net. Based on the person model and the direction of information flow, the EPM is established subsequently. The EPM consists of several release levels, in which the access controls are defined. The EPM procedure shows the blueprint of the workflow process structure. The establishment of person model and EPM in an enterprise has been instanced at the end of this paper.

In order to better realize agricultural informatization, a data management system for agricultural supply chain, which has the characteristics of safe, credible, stable, traceable, information-sharing, and large-throughput, is needed to construct. To date, the information management method for China's agricultural supply chain is usually stored in a centralized database and file system, with weak information management capabilities, leading to problems such as theft, tampering, deletion, and inconsistencies. In light of these problems, we introduce blockchain technology which is a cutting-edge technology in digital finance and has developed rapidly. This paper proposes a data management system based on blockchain technology and affords efficient data extraction, management and access control for heterogeneous forms of data across the agricultural supply chain. The data management system includes four credible data management platforms: agricultural production information, recording transportation information, farmer-consumer transaction information, and consumer credit information. It also can add more platforms according to later needs and combine an interstellar distributed file system and smart contract technology, making it possible to conduct information security research throughout the agricultural supply chain. The proposed system effectively protects information about supply chain activities including agricultural product production, warehousing, transportation, distribution, and sales. It implements seamless connection between agricultural product production and marketing, enabling the transforming and upgrading of agriculture, thereby helping farmers increase their income and eliminate poverty.

Multimedia social networks(MMSNs) have provided a convenient way to share the multimedia contents such as images, videos, blogs, etc. online. However, due to various privacy concerns people often want to selectively share the contents with their friends, family, colleagues, etc. Access control mechanisms help in this situation by deciding the individual who can access the contents and who cannot. But traditional access control mechanisms may not scale well for fine grained access control requirements (e.g. different access control parameters for different parts in a picture) and large number of users in MMSNs. Thus, an efficient access control mechanism suitable for MMSNs is required. In this paper, we present an efficient bit-vector transform based access control mechanism suitable for MMSNs. Mathematical analysis and experimental results show the effectiveness of our proposed approach.

ABSTRACTMost archaeological investigations in the United States and other countries must comply with preservation laws, especially if they are on government property or supported by government funding. Academic and cultural resource management (CRM) studies have explored various social, temporal, and environmental contexts and produce an ever-increasing volume of archaeological data. More and more data are born digital, and many legacy data are digitized. There is a building effort to synthesize and integrate data at a massive scale and create new data standards and management systems. Taxpayer dollars often fund archaeological studies that are intended, in spirit, to promote historic preservation and provide public benefits. However, the resulting data are difficult to access and interoperationalize, and they are rarely collected and managed with their long-term security, accessibility, and ethical reuse in mind. Momentum is building toward open data and open science as well as Indigenous data sovereignty and governance. The field of archaeology is reaching a critical point where consideration of diverse constituencies, concerns, and requirements is needed to plan data collection and management approaches moving forward. This theme issue focuses on challenges and opportunities in archaeological data collection and management in academic and CRM contexts.

In order to cope with the pressure and challenges brought by the growing number of tourists in China, the development of big data and related electronic information technology provides new opportunities for the operation and management of scenic spots. On the premise of analyzing the relevant background of big data, the advantages of the application of big data in the management of tourist attractions, tourism marketing, tourism operation status and the shaping of tourist reputation are proposed, which provides technical support, data support and intellectual support for the realization of efficient operation, management, analysis, prediction and evaluation of scenic spots. Combining with the application status of big data in the tourism industry, firstly, the paper proposes to establish a data storage and management system based on cloud computing technology, which provides high-performance services for the storage and management of tourism big data. Secondly, it promotes the application of Internet of Things technology in tourism and realizes the intelligent management of tourist attractions. The third is the establishment of the travel information center service platform, for the calculation and processing of data, massive high data access and query to provide a guarantee. The fourth is the establishment of a protection system for tourism data security through active defense, physical isolation and other technical means to provide multi-level and all-round protection for tourism data security.

A High Level Interface Language for GIS

This paper presents a graphical query language for geographic information systems called Cigales. The basic idea of this language is to express a query by drawing a pattern that corresponds to the results the user wants to be displayed. This drawing is in accordance with its representation of the real world. A set of active icons compose graphical forms, which are the representation of geometric objects (e.g., line and area), and operations such as inclusion and intersection. The graphical query is then translated into a functional language. This intermediate language guarantees the independence of Cigales from the data management system (DBMS). The user need not worry about physical storage of data and does not have to learn a new language. The functional expression is then optimized and translated into specific DBMS commands (e.g., extended SQL). This paper also describes an extended SQL of a geographic DBMS, which was used as a first platform for the implementation of Cigales. The advantages and limits of using a graphical approach are discussed.

Cloud computing leaped helping industries with scalable, flexible, and cheaper solutions to manage large datasets and complex computations. The utility sector benefited quite a lot from combining cloud computing and data analytics to improve operational efficiency, customer service, and decision-making processes. The scope of this paper is to imply how cloud computing has aided in modernizing data analytics for the utility industry. Utilities are being allowed, through the power of cloud technologies, to circumvent some of the traditional problems associated with on-premises data storage and management systems that are limited in scalability and maintenance cost, with no data processing capability.

Reliance on data for software systems engineering is increasing, e.g., to train machine learning applications. We foresee increasing costs for data collection and maintenance, leading to the risk of development budgets eaten up by commodity features, thus leaving little resources for differentiation and innovation. We therefore propose Open Collaborative Data (OCD) - a concept analogous to Open Source Software (OSS) - as a means to share data. In contrast to Open Data (OD), which e.g., governmental agencies provide to catalyze innovation, OCD is shared in open collaboration between commercial organizations, similar to OSS. To achieve this, there is a need for technical infrastructure (e.g., tools for version and access control), licence models, and governance models, all of which have to be tailored for data. However, as data may be sensitive for privacy, anonymization and obfuscation of data is also a research challenge. In this paper, we define the concept of Open Collaborative Data, demonstrate it by map data and image recognition examples, and outline a research agenda for OCD in software engineering as a basis for more efficient evolution of software systems.

Authorization plays an essential role to ensure the security of a wide variety of computing and IT systems such as data management systems, e-trading systems, database transaction systems, etc. This paper aims to propose a high level formal language for specifying and evaluating distributed authorizations with delegation, develop a new method for credential chain discovery, and implement a system prototype for representing and reasoning about access control policies in distributed environments. By applying the new methodology and technology developed from this work, we will be able to design highly secure computing and IT systems in many different complex problem domains.

Discovering and developing new drugs/ medicines is very crucial for the pharmaceutical industry. The increasing number of drugs approved in recent years demonstrates the impact of modern drug discovery approaches, digital technologies, and automated drug development methodologies. Drug development is a systematic and methodological process of developing a new pharmaceutical drug once the process of Drug discovery has identified the prime pharmacological component. The structured sequence of steps followed for drug development aims to ensure the safety and efficacy of the drug being developed. It includes pre-clinical research on microorganisms and animals, preparation of detailed data with respect to pharmacology, pharmacokinetics and toxicology details, application and approval by regulatory authorities and conduction of clinical trials. The conduction of clinical trials is an expensive affair as it needs a collaborative effort by multiple stakeholders along with a high level of monitoring and regulation. The data generated during the lifecycle of clinical trials is very critical for pharmacological scientific publications, regulatory approval for the target drug and post-marketing surveillance that ultimately leads to the development of better decision support systems for drug development. Hence, the data integrity of such data is of prime importance. Several Clinical data management (CDM) systems have been developed to ensure seamless collection and management of clinical trial data. These CDM systems enable useful analysis and decisions supported by authentic data. However, such systems face several security challenges with respect to privacy, integrity and authenticity of the clinical data. Another major challenge in conducting the clinical trials is finding the appropriate willing candidate who is physically and clinically suitable for the study. In view of the above, it is highly desirable to have a technology component that can address the above-mentioned issues. In this chapter, the technologies like blockchain and cloud computing have been introduced to address the challenges posed by clinical trial data management. The paper also proposes a blockchain based secure clinical data management system. The proposed system intends to help the data security issues like data integrity, privacy, ease and quick access to immutable clinical trial data with thorough access control enabling greater transparency and accountability.

The diversity of data management systems affords developers the luxury of building systems with heterogeneous systems that address needs that are unique to the data. It allows one to mix-n-match systems that can store, query, update, and process data, based on specific use cases. However, this heterogeneity brings with it the burden of developing custom interfaces for each data management system. Developers are required to build high-performance APIs for data access while adopting best-practices governing security, data privacy, and access control. These include user authentication, data authorization, role-based access control, and audit mechanisms to avoid compromising the security standards mandated by data providers. In this paper, we present Bindaas, a secure, extensible big data middleware that offers uniform access to diverse data sources. By providing a standard RESTful web service interface to the data sources, Bindaas exposes query, update, store, and delete functionality of the data sources as data service APIs, while providing turn-key support for standard operations involving security, access control, and audit-trails. Bindaas consists of optional features, such as query and response modifiers as well as plugins that implement composable and reusable data operations on the data. The research community has deployed Bindaas in various production environments in healthcare. Our evaluations highlight the efficiency of Bindaas in serving concurrent requests to data source instances. We further observe that the overheads caused by Bindaas on the data sources are negligible.

Data and analytics are indispensable parts of any successful business. Despite that, they can lead to serious privacy issues if not used safely. Even though the financial and legal consequences of privacy breaches are profound, the most damaging one, in the long run, is customer distrust. This is particularly the case for hypersensitive data such as personal health information. In recent years, the significance of privacy, security, and trust to the design of successful modern systems are being recognized more than ever before. Nevertheless, the current state of data management systems is far from achieving this goal. Particularly, in medical data management systems, handling sensitive data, mistrust is a barrier to many core processes such as data collection, sharing and analytics. Consequently, it is a primary cause of suboptimal medical services in our time. The primary direction of this thesis is to respond to these concerns of ethical nature by taking data privacy as a fundamental human right into serious consideration in the design of these processes. We highlight the emerging role of the blockchain in health information systems and recognize blockchain's ample opportunities for trust establishment. Consequently, we propose solutions based on this novel technology. More precisely, this thesis proposes a privacy-preserving data market for incentivedriven data collection for the development of reliable mathematical models that accelerate medical research. The blockchain aspect of the platform provides transparency as to the purpose for which the data is used to avoid the perception of data misuse/abuse. Secondly, this thesis proposes flexible blockchain-based access control mechanisms with multiple privacy protection layers for user-centric data sharing. Furthermore, it proposes a distributed machine learning-as-a-service platform for data analytics whose computational integrity can be verified efficiently by users. The strength of this platform lies in mutual privacy protection of user data and service provider intellectual property.