Using Distributed Ledger Technologies to Support Complex Supply Chains

The concept of blockchain, as part of distributed ledger technologies, has gained a lot of interest recently, especially in cryptocurrencies. With the addition of other technical capabilities, e.g., smart contracts and oracles, this interest has spread to other areas as well and affects a wide variety of business processes such as supply chain processes. However, in research, the wide variety of processes finds inadequate consideration to date. In this research paper, we provide an overview of the state of the art of distributed ledger technologies in supply chains and point out future research topics. Therefore, we conducted a structured literature review, systematized potential application areas in supply chain processes, and showed that research gaps exist. To address the research gaps, we derived open research questions, whereby conducting design studies to deal with the practical problems in the application areas plays a central role

Block CUR: Decomposing Matrices using Groups of Columns

A common problem in large-scale data analysis is to approximate a matrix using a combination of specifically sampled rows and columns, known as CUR decomposition. Unfortunately, in many real-world environments, the ability to sample specific individual rows or columns of the matrix is limited by either system constraints or cost. In this paper, we consider matrix approximation by sampling predefined \emph{blocks} of columns (or rows) from the matrix. We present an algorithm for sampling useful column blocks and provide novel guarantees for the quality of the approximation. This algorithm has application in problems as diverse as biometric data analysis to distributed computing. We demonstrate the effectiveness of the proposed algorithms for computing the Block CUR decomposition of large matrices in a distributed setting with multiple nodes in a compute cluster, where such blocks correspond to columns (or rows) of the matrix stored on the same node, which can be retrieved with much less overhead than retrieving individual columns stored across different nodes. In the biometric setting, the rows correspond to different users and columns correspond to users' biometric reaction to external stimuli, {\em e.g.,}~watching video content, at a particular time instant. There is significant cost in acquiring each user's reaction to lengthy content so we sample a few important scenes to approximate the biometric response. An individual time sample in this use case cannot be queried in isolation due to the lack of context that caused that biometric reaction. Instead, collections of time segments ({\em i.e.,} blocks) must be presented to the user. The practical application of these algorithms is shown via experimental results using real-world user biometric data from a content testing environment.Comment: shorter version to appear in ECML-PKDD 201

Multi-agent system to assure the logical security of data in distributed information system

The increased availability of information as a whole became an important problem and threat for its security, especially security of sensitive and confidential information and that is why the necessity to assure the security of such data became undeniable. The developers of applications an information systems put more and more stress on the aspect of their security and safety. Development of information systems has to answer more and more to problems connected to federated data sources and problems of heterogeneous distributed information systems. It is necessary to propose the architecture for secure cooperation of such systems. The paper presents the practical application of concepts of multi-agent systems in domain of logical security of data in distributed information systems. The purpose of presented solution is to support the process management of IT project realization based on the software creation methodologies

Artificial chemistry approach to exploring search spaces using artificial reaction network agents.

The Artificial Reaction Network (ARN) is a cell signaling network inspired representation belonging to the branch of A-Life known as Artificial Chemistry. It has properties in common with both AI and Systems Biology techniques including Artificial Neural Networks, Petri Nets, Random Boolean Networks and S-Systems. The ARN has been previously applied to control of limbed robots and simulation of biological signaling pathways. In this paper, multiple instances of independent distributed ARN controlled agents function to find the global minima within a set of simulated environments characterized by benchmark problems. The search behavior results from the internal ARN network, but is enhanced by collective activities and stigmergic interaction of the agents. The results show that the agents are able to find best fitness solutions in all problems, and compare well with results of cell inspired optimization algorithms. Such a system may have practical application in distributed or swarm robotics

Enhancing Mobile Agent Security Level (Proposed Model)

Mobile agents are application design schemes for distributed systems that consist of mobile code ideology including Mobile agent software. In the last period mobile computing process had a vision that’s a set of execution code that’s move from platform to another in the heterogeneous network with an ability of carrying there result and updating them self-sate. This paper presents several enhancements on mobile agent security and provides generalized code protection. Several novel techniques are proposed to protect mobile agents in any environments and to describe and solve practical problems in the mobile agent system

The Performance of Distributed Applications: A Traffic Shaping Perspective

Widely used in datacenters and clouds, network traffic shaping is a performance influencing factor that is often overlooked when benchmarking or simply deploying distributed applications. While in theory traffic shaping should allow for a fairer sharing of network resources, in practice it also introduces new problems: performance (measurement) inconsistency and long tails. In this paper we investigate the effects of traffic shaping mechanisms on common distributed applications. We characterize the performance of a distributed key-value store, big data workloads, and high-performance computing under state-of-the-art benchmarks, while the underlying network's traffic is shaped using state-of-the-art mechanisms such as token-buckets or priority queues. Our results show that the impact of traffic shaping needs to be taken into account when benchmarking or deploying distributed applications. To help researchers, practitioners, and application developers we uncover several practical implications and make recommendations on how certain applications are to be deployed so that performance is least impacted by the shaping protocols