Добірка наукової літератури з теми "Code-sharing Agreements"

Data integration is a basic need for a CASE environment because individual tools operate on data that are of interest to other tools. For example, many CASE analysis and design tools produce code templates representing the interfaces between system components. These templates are of interest to tools that assist in code generation since they specify an interface between system components that ideally is maintained during the implementation process. Likewise, changes made to these interface descriptions during the implementation process often have an impact on the design of the system as reflected in the analysis and design tool. Many approaches have been developed to facilitate the sharing of data between components of a CASE environment. These approaches differ both in the mechanisms used to provide this support and in the degree of support provided. In this chapter we consider some concepts central to the problem of data integration, discuss the major strategies adopted to provide data integration, and analyze the strengths and weaknesses of these strategies. Finally, we discuss particular mechanisms that reflect these strategies. If environment components are to share data, then two issues must be addressed. First, agreements must be made between components concerning what data are stored, where, and how they are accessed. Second, the components must share a common understanding of the meaning of the data. We refer to the first issue as data persistence, and the second issue as data semantics. These two issues provide the backdrop for our discussion of the principal types of mechanisms for data integration. One type of mechanism focuses on different storage strategies, and the second type of mechanism focuses on semantic agreements. Although we consider each separately, no mechanism exclusively addresses only data persistence or data semantics. In practice, all mechanisms address both data persistence and data semantics to varying degrees. There are two basic strategies that support storage and sharing of persistent data. The first involves data import and export, whereby tools maintain separate databases for their unique data, and sharing is accomplished by some translation from an internal form to an external form to make it available to other tools.

The concept of eLearning has grown exponentially in recent years. Indeed, what was but a few decades ago exclusively reserved for classroom attendees of university institutions and their closed intranets has today become a publicly accessible source of information, with public and private-owned eLearning MOOC (Massive Open Online Courses) platforms dominating the digital landscape and bringing with them a plethora of services such as video courses, certifications and employee recruitment. Nonetheless, while adding significant public value, such platforms still fail to completely bridge the gap between academia and industry in the hands-on knowledge application sense. This is especially true for domains such as machine learning that are known to require tremendous computational resources if one wishes to transition from simplistic lab exercises to complex state-of-the-art models, which is something a MOOC - or most academic institutions for that matter - simply cannot afford. Private cloud providers have taken up the challenge here and opened their offerings to the research community with programs such as TensorFlow Research Cloud, CNTK on Microsoft Azure, MXNet on Amazon AWS, to name but a few. Nonetheless, access to such resources remains limited: either the resources themselves are scarce and consequently freely available only to a restricted research community, or they are free for a limited time, or they follow a freemium model. This paper describes a novel distributed publicly-hosted overlay network that provides free collaborative eLearning material and computational resources to its users. Unlike most of its P2P counterparts in existence today that address alternative public needs (i.e. anonymity, file sharing, ...), it offers unlimited runtime access to all the computational resources that collectively define its topology. In this respect, an example use case is one wherein a distributed machine learning system can be orchestrated by any user and freely deployed for execution on any number of available nodes within the network, without any limitation other than that of the underlining hardware and network latency performance capabilities themselves. Additionally, source code and accompanying eLearning courses are regularly published to the network and made freely available by users belonging to the academic and subject matter expert practitioner community, implementing solutions ranging from simple classroom exercises to entire production-ready systems. The network is collaborative by design, such that learning material can be collectively worked on and published, training data can be shared and successfully deployed software solutions can be load-balanced and made accessible to the entire community. Moreover, the distributed network follows a decentralized model governed by a handful of master nodes that, similarly to all its other nodes, are hosted by the users themselves, and in this sense constitutes a novel cloud offering maintained by the general public rather than any given private cloud provider. Finally, we argue that beyond its educational and computational power offerings, the described network likely constitutes the best test platform for any designed software system: indeed, given the volatile nature of the network (wherein user-hosted resources can join or go offline at any moment), we claim that any software system that can "survive" and maintain its levels of SLA (service level agreement) on this network is guaranteed to perform similarly or better on a more traditional pay-per-usage cloud provider infrastructure.