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The accessible, non-technical guide to applying and benefiting from blockchain technology. Blockchain has grown at an enormous rate in a very short period of time. In a business context, blockchain can level the playing field between small and large organisations in several ways: Exact copies of the immutable, time-stamped data is held by all parties, all transactions can be viewed in real time, data blocks are cryptographically linked, all raw materials are traceable and smart contracts ensure no middle-men, ease of audit and reduced friction. The trust, transparency, security, quality and reduced costs of blockchain make it a game-changing technology that crosses sectors, industries and borders with ease. Even though the technologies are ready for adoption, businesses remain largely unaware of their full potential and effective implementation. End users require accurate and up-to-date information on the practical applications of blockchain-Commercializing Blockchain provides it. A practical and easy-to-understand guide to blockchain, this timely book illustrates how this revolutionary technology can be used to transform governments, businesses, enterprises and entire communities. The author draws from his experience with global retailers, global technology companies, UCL Centre for Blockchain technologies, the government of the UK, Retail Blockchain Consortium and many other sources to present real-world case studies on the use and benefits of blockchain. Topics include financial transactions, tokenisation, identity management, supply chain transparency, global shipping and freight, counterfeiting and more. Provides practical guidance for blockchain transactions in business operations -Provides practical guidance for blockchain transactions in business operations -Demonstrates how blockchain can add value and bring increased efficiency to commercial operations -Covers all of the essential components of blockchain such as traceability, provenance, certification and authentication -Requires no technical expertise to embrace blockchain strategies Commercializing Blockchain: Strategic Applications in the Real World is ideal for enterprises seeking to develop and deploy blockchain technology, particularly in areas retail, supply chain and consumer goods.

Frequent itemset mining (FIM) is a major component in association rule mining, significantly influencing its performance. FIM is a computationally intensive nondeterministic polynomial time (NP)-hard problem. At the core of FIM is the task of computing support of candidate itemsets. This problem becomes more severe when the dataset is dense as the support is computed for millions, or even billions, of candidate itemsets. The rapid growth of data further exacerbates this problem. To achieve high scalability and efficiency, recently, researchers have proposed various approaches to approximate the support of an itemset using as small a subset of transaction data as possible. In addition to efficiency, accuracy is another important metric for these algorithms. They strive to increase true positives and reduce false negatives and false positives. One such recently proposed approximate FIM algorithm is Probabilistic Breadth-First (ProbBF), which is highly efficient for dense data due to its unique approach of not using transactional data beyond 2-size itemsets. Unlike other counterparts, this algorithm requires no additional input parameters beyond the traditional support threshold. However, ProbBF is a breadth-first algorithm, and it is well-established that breadth-first FIM algorithms consume significantly more memory than depth-first algorithms on dense datasets. It is also worth noting that significantly high memory consumption slows run-time performance of an algorithm due to low utilization of locality of reference, thrashing, and aggressive garbage collection etc . This article proposes a FIM algorithm, ProbDF, that discards transaction data after determining all frequent itemsets of sizes one and two. For frequent itemsets of size three or more, it employs a probabilistic support prediction model (PSPM) to predict their support probabilistically. PSPM, first proposed with ProbBF, uses lightweight calculations that exclude transaction data. Our experiments demonstrate that ProbDF, with its depth-first search strategy tailored to PSPM and other optimizations, is efficient in terms of time and space, and successfully generates the majority of frequent itemsets on real-world benchmark datasets. However, due to the probabilistic nature of ProbDF, some compromise in quality is inevitable.

\"After Ethereum creator Vitalik Buterin dropped out of college and launched Bitcoin Magazine, he wrote the Ethereum white paper, which proposed an open source system that would take what Bitcoin did for money and do it for everything else: contracts, social networks, and sharing economies. Now, less than a decade later, his idea is valued at about half a trillion dollars, and it is the foundation for the weird new world of NFT artworks, virtual real estate, and decentralized autonomous organizations. Understanding and engaging with Buterin's ideas will be of growing importance as the consequences of his invention continue to unfold and inspire debate worldwide. These writings, collected from his essays before and during the rise of Ethereum, reveal Buterin to be a vivid and imaginative writer, and this edition includes context from media studies scholar Nathan Schneider. While many around him were focused on seeing the value of their tokens rise, Buterin was working through the problems and possibilities of crafting an Internet-native world\"-- Provided by publisher.

Time profiled association mining is one of the important and challenging research problems that is relatively less addressed. Time profiled association mining has two main challenges that must be addressed. These include addressing i) dissimilarity measure that also holds monotonicity property and can efficiently prune itemset associations ii) approaches for estimating prevalence values of itemset associations over time. The pioneering research that addressed time profiled association mining is by J.S. Yoo using Euclidean distance. It is widely known fact that this distance measure suffers from high dimensionality. Given a time stamped transaction database, time profiled association mining refers to the discovery of underlying and hidden time profiled itemset associations whose true prevalence variations are similar as the user query sequence under subset constraints that include i) allowable dissimilarity value ii) a reference query time sequence iii) dissimilarity function that can find degree of similarity between a temporal itemset and reference. In this paper, we propose a novel dissimilarity measure whose design is a function of product based gaussian membership function through extending the similarity function proposed in our earlier research (G-Spamine). Our approach, MASTER (Mining of Similar Temporal Associations) which is primarily inspired from SPAMINE uses the dissimilarity measure proposed in this paper and support bound estimation approach proposed in our earlier research. Expression for computation of distance bounds of temporal patterns are designed considering the proposed measure and support estimation approach. Experiments are performed by considering naïve, sequential, Spamine and G-Spamine approaches under various test case considerations that study the scalability and computational performance of the proposed approach. Experimental results prove the scalability and efficiency of the proposed approach. The correctness and completeness of proposed approach is also proved analytically.

Recent research on automated trading algorithms has focused on evaluating their effectiveness on various financial markets and comparing their performance with that of other trading methods. This paper proposes an innovative framework for the simulation of trading algorithms with the purpose of supporting the development of automated decision systems for traders using agent-based modeling and reinforcement learning methods in a market context. The obtained results demonstrate that a trading agent can learn and optimize the trading strategies, even if performance variations were observed in relation to environmental changes. The success of the trading agent depends on the commitment of trading partners and the implementation of risk management in correlation with the market norms. The proposed model provides a valuable platform for further studies on learning behavior in trading based on the decisions of a real trader.

Most of the existing privacy-preserving techniques, such as k-anonymity methods, are designed for static data sets. As such, they cannot be applied to streaming data which are continuous, transient, and usually unbounded. Moreover, in streaming applications, there is a need to offer strong guarantees on the maximum allowed delay between incoming data and the corresponding anonymized output. To cope with these requirements, in this paper, we present Continuously Anonymizing STreaming data via adaptive cLustEring (CASTLE), a cluster-based scheme that anonymizes data streams on-the-fly and, at the same time, ensures the freshness of the anonymized data by satisfying specified delay constraints. We further show how CASTLE can be easily extended to handle ℓ-diversity. Our extensive performance study shows that CASTLE is efficient and effective w.r.t. the quality of the output data.

Frequent subgraph mining algorithms are widely used in various areas for information analysis. As yet, a handful of algorithms have been proposed and defined in the literature. While several experimental studies were reported, these experiments lack critical information which are important for selecting an implementation of an algorithm for a specific case of use. In this paper, we report on experiments that we carried out on available implementations of complete search Frequent Subgraph Mining (FSM) algorithms. These experiments are conducted in order to choose a suitable FSM solution (i.e., implementation). We identified 32 algorithms in the literature, six of them were selected for our experiments, through a filtering process relying on a set of criteria. Thirteen working implementations of these 6 algorithms are experimented. In this paper, we provide details of the experiments in terms of performance metrics and input variation effect. We propose a preliminary selection of the most efficient FSM solutions for end users, based on the most tested centralized graph-transaction datasets of the literature.

Optimally assigning streaming tasks to network machines is a key factor that influences a large data-stream-processing system's performance. Although researchers have prototyped and investigated various algorithms for task placement in data stream management systems, taxonomies and surveys of such algorithms are currently unavailable. To tackle this knowledge gap, the authors identify a set of core placement design characteristics and use them to compare eight placement algorithms. They also present a heuristic decision tree that can help designers judge how suitable a given placement solution might be to specific problems.

The trading risk management implies analysing several types of risks: capital, market, liquidity, insolvency, business, credit, operational or financial risk. Trading models and techniques must be seen as tools that can provide an informed manager with useful insights, so they are indispensable in an increasingly integrated and sophisticated market. The main aim of this study is the conceptualization of a generic intelligent agent, based on a neural network and an agent system, applicable to a trading system of the listed companies. The results show that this model can contribute to reducing the transactional risk on the capital market and can offer solutions to improve the managerial decisions.