The proliferation of connected IoT devices creates a big data problem for AI based approaches. The response time required by such devices necessitates IoT data to be processed at the edge, but the edge typically lacks the resources to learn the AI models. We present an architecture which preserves the advantages of both edge processing and server-based/cloud-centric computing for AI algorithms. We discuss how policy management can be used to improve and support this architecture.

Next-generation collaborative activities and missions will be carried out by autonomous groups of devices with a large variety of cognitive capabilities. These devices will have to operate in environments characterized by uncertainty, insecurity (both physical and cyber), and instability. In such environments, communications may be fragmented. Proper policy-based management of such autonomous device groups is thus critical. However current policy management systems have many limitations, including lack of flexibility. In this paper, we articulate novel architectural approaches addressing the requirements for the effective management of autonomous groups of devices and discuss the notion of generative policies - a novel paradigm that enhances the flexibility of policy-based approaches to management. In this paper, we also survey types of policy that are essential for managing device groups. Even though many such policy types exist in conventional settings, their use in our context poses novel challenges that we articulate in the paper. We also introduce a research roadmap discussing several research directions towards the development of a cognitive and flexible policy-based approach to the management of autonomous groups of devices for collaborative missions. Finally, as our proposed policy paradigm is data-intensive, we discuss the problem of supplying the data required for policy decisions in environments characterized by mobility, uncertainly, and fragmented communications.

Successful collaborations require information and resource sharing and thus adequate access control policy management systems that control sharing among the collaborating entities. Such management systems need to be flexible in order to adapt to different environments and thus be able to support access control policy evolution. However, when dealing with large sets of evolving policies it is critical that policies meet certain "policy quality requirements". Specifically, policies of interest must be up-to-date, complete, free of inconsistencies, relevant. In this paper, we propose an approach to analyze policies in order to determine whether policies meet such requirements. Our approach is based on the use of provenance techniques that collect comprehensive data about actions executed by users in the context of workflows, that is, sets of tasks executed according to some ordering by users. Provenance data are used by services that support various types of analysis to determine whether the policies of interest verify the quality requirements.

No abstract available.

Article Collaborative customer services using synchronous Web browser sharing Share on Authors: Makoto Kobayashi IBM Research, Tokyo Research Laboratory, 1623-14, Shimotsuruma, Yamato, Kanagawa 242-8502, Japan IBM Research, Tokyo Research Laboratory, 1623-14, Shimotsuruma, Yamato, Kanagawa 242-8502, JapanView Profile , Masahide Shinozaki IBM Research, Tokyo Research Laboratory, 1623-14, Shimotsuruma, Yamato, Kanagawa 242-8502, Japan IBM Research, Tokyo Research Laboratory, 1623-14, Shimotsuruma, Yamato, Kanagawa 242-8502, JapanView Profile , Takashi Sakairi IBM Research, Tokyo Research Laboratory, 1623-14, Shimotsuruma, Yamato, Kanagawa 242-8502, Japan IBM Research, Tokyo Research Laboratory, 1623-14, Shimotsuruma, Yamato, Kanagawa 242-8502, JapanView Profile , Maroun Touma IBM Thomas J. Watson Research Center, 30 Saw Mill River Rd., Hawthorne, NY IBM Thomas J. Watson Research Center, 30 Saw Mill River Rd., Hawthorne, NYView Profile , Shahrokh Daijavad IBM Thomas J. Watson Research Center, 30 Saw Mill River Rd., Hawthorne, NY IBM Thomas J. Watson Research Center, 30 Saw Mill River Rd., Hawthorne, NYView Profile , Catherine Wolf IBM Thomas J. Watson Research Center, 30 Saw Mill River Rd., Hawthorne, NY IBM Thomas J. Watson Research Center, 30 Saw Mill River Rd., Hawthorne, NYView Profile Authors Info & Claims CSCW '98: Proceedings of the 1998 ACM conference on Computer supported cooperative workNovember 1998 Pages 99–109https://doi.org/10.1145/289444.289484Published:01 November 1998 31citation820DownloadsMetricsTotal Citations31Total Downloads820Last 12 Months3Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

This paper describes an empirical study that compared three alternatives for voice communication in conjunction with Web page collaboration for customer service.Two of the technologies used a single phone line for both voice and data transmission.These technologies were intemet telephony and Simultaneous< Voice and Data (SVD), a protocol which allows the voice to be routed over the public telephone network, rather hart the inteme~The study found that SVD was superior to intemet telephony in terms of a number of behavioral and subjective measures of con\7ersational interaction.The study also found that task time using intemet telephony was 45% greater than with SVD, mtilng the former a costly rdtemative in terms of human time.