Real-time Transactions Research Articles

The Last Man Standing Technique for Proof-of-Location in IoT Infrastructures at Network Edge

This paper is divided in two parts. The first part is the story of a failure, where an attempt to generate verifiable Proof-of-Location from WiFi scan results has failed, even when scan results were compared with a small time shift within the same trace. In the second part, this paper proposes to conduct Proof-of-Location transactions in a peer-to-peer fashion. Each peer is assisted by the cloud side which plays the role of both the real-time mediator and public transaction ledger. This paper proposes the Last Man Standing (LMS) procedure which is both a means for ensuring a fair transaction and a natural way to close it. Each transaction results in a coin which can be either shared among transaction participants or owned individually by LMS. Analysis using real mobility traces from various types of urban locations shows that the proposal is valid and will ensure that all the locations within the city will gradually be claimed via the proposed type of transactions while providing independently verifiable proofs for each location. The distant goal of this paper is a next generation Location-Based Service (LBS) which takes the form of a location-based resource economy where each location is a coin compatible with traditional blockchain operations.

Banking on Stone Money: Ancient Antecedents to Bitcoin

Centuries ago in western Micronesia, Yapese islanders sailed to the Palauan archipelago 250 miles away to carve their famous stone money disks (rai) from limestone and then transported them back for use as exchange valuables in various social transactions. While rai were not strictly currency, their value is similar to other traditional and modern objects where worth is arbitrarily based on both real and perceived attributes. Recently, corollaries have been made between rai and newly established electronic cryptocurrencies that use blockchain technology—essentially, digital ledgers that track financial transactions in real time across a computer network to ensure that they are seamless and incorruptible. In this study, we examine the sociophysical similarities and differences between stone money and cryptocurrencies that rely on blockchain, both of which demand a unified and continuous chain of information to ensure that the value is known and ownership indisputable. This research suggests that stone money: (a) is an exemplary ancient analog to blockchain that used oral ledgers solidified through social networks to create accurate and unbroken lines of communication so that economic relationships involving rai could be established, maintained, exchanged, and rectified, and (b) may have been a progenitor that inspired the development of Bitcoin.

Security Issues on Blockchain Network (BcN): A Review

Recent days the Blockchain attracts attention from many researchers and in the investment industries. With the increasing usage of Blockchain in physical transactions, it is also very needful to secure the blockchain network (BcN). If inaccurate results are generated because of an insecure network, then it will affect the real-time data. This paper discussed on several research works on Blockchain. Two major architecture of Blockchain Network is discussed in this paper; one is Parallel Blockchain Network (PBcN) and another one is Series Blockchain Network (SBcN). Two major techniques, Proof-of-Work (PoW) and Proof-of-Stake (PoS) are considered and conversed with the conventional algorithms with their consequent limitations. At the end of the paper, it is concluded with the straightforwardness that, as an emerging area, blockchain embraces a lot of upcoming prospects that can be used in real-time operation; but still, this area restrains plenty security concerns for which real-time transaction may not succeed. Hence it is very essential to streamline the safety measures of the Blockchain Network (BcN) in the upcoming research.

“Super Service Delivery”: an advanced conceptual model of one-stop service for wide administrative region

The innovative role of “One-Stop Integrated Services” (one-stop service in Indonesia) is now being developed. On the other hand, the problems faced by the community in their efforts to obtain service have not been decomposed precisely. The issue of distance and travel time to the service center (service offices: OSS) and the resulting costs are still burdensome. This situation is very prevalent in communities in districts, cities, and provinces, because Indonesia’s topography is divided into land administration areas and large islands.The aim is to unravel the burden of problems faced by the community of service users in the land administration area and large islands. The authority, main tasks, and functions of the “one-stop integrated service office” can be stretched close to the residents of far-flung communities in remote areas, because the original public service users are there.Technically, the tasks and functions of PTSP services are delegated through sub-district offices in various parts of the region, and may even be delegated to the village offices/village offices. PTSP parties only need to place 1 (one) up to 2 (two) personnel to carry out this task. This technique administratively utilizes government work networks and e-gov networks that are ready and able to mediate recording and transactions in real time. Regional Government Banks can also provide support in the same way to handle the management of super service delivery transactions in various parts of the district.

A Deep Dive: Does Big Data Improve Maturity in the Developed Capital Markets?

Over this decade, the concept of big data has been applied to industries but the capital markets have been traditionally laggard to adoption. Within the financial services’ sector, Big Data has gained far more traction within retail banking and insurance due to the increasing desire of these financial institutions to profile and analyze their customers in a similar manner to early adopters of Big Data strategy such as Amazon, Baidu or Google. However, Big Data strategies have begun to make some impacts on few selected areas of the capital markets, including the social media sentiment analysis on the structured and unstructured data for trading, growth in volume, risk analytics, fraud prevention, market surveillance, predictability and forecasting of the equity prices; those are the early sign of the maturity of the capital markets. Technical and theoretical measures have evolved, but still these dimensions of the capital markets have been a mystery for the human beings till now. The Big Data in the form of structured, semi-structured and unstructured socio-economic and demographic information from social media and blogs from consumers has started indicating impacts on the capital markets which can lead to improving the real-time systems and transaction processing, and improving operational efficiency and maturity. The intent of this paper is threefold. First, it aims to bring the clear inference from the past researches to take a holistic analysis of the work done in the emerging area of Big Data and its implications on capital markets. Second, it’s to perform a deep analysis on how the influences of Big Data affect the assumptions in connection with Random Walk theory and Efficient Market Hypothesis. Third, it will provide a conclusive theoretical analysis of past research work by the scholars, which can establish the model to refine the nexus between investors’ sentiments and assets’ prices with advanced techniques in the Big Data. The paper has been divided into 4 broad sections. In the first section, the paper sets the introduction of connecting the dots and setting the context for the two different fields like Big Data and its influences on the capital markets. The second section explains the theoretical premises and frameworks needed for this research and does deep studies of the previous works in this area to establish conclusive references for the future study. The third section carries out the studies of emerging social media and technologies, analysis of the previous research works from the social media and the capital markets perspective. Finally, the fourth section concludes findings with recommendations.

Using isolation forest in anomaly detection: The case of credit card transactions

With the evolution of new technology especially in the domain of e-commerce and online banking, the payment by credit card has seen a significant increase. The credit card has become the most used tool for online shopping. This high rate in use brings about fraud and a considerable damage. It is very important to stop fraudulent transactions because they cause huge financial losses over time. The detection of fraudulent transactions is an important application in anomaly detection. There are different approaches to detecting anomalies namely SVM, logistic regression, decision tree and so on. However, they remain limited since they are supervised algorithms that require to be trained by labels in order to know whether the transactions are fraudulent or not. The goal of this paper is to have a credit card fraud detection system which is able to detect the highest number of new transactions in real time with high accuracy. We will also compare, in this paper, different unsupervised techniques for credit card fraud detection namely LOF, one class SVM, K-means and Isolation Forest so as to single out the best approach.

Hybrid blockchain and pseudonymous authentication for secure and trusted IoT networks

This paper addresses the issue of secure and trusted Internet of Things (IoT) networks by adopting the emerging blockchain technologies. This paper proposes a new hybrid blockchain technology to address the trusted IoT issues such as trustless communications and decentralized applications. Besides, we also present that the pseudonymous authentication technique can use a puzzle-solving computation to enable trustless communications for the IoT and provide the capabilities of near real-time transactions. In our previous work, we presented a decentralized software framework for the IoT by using a p2p network and the concept of the blockchain. In this paper, we outline the core components of the hybrid blockchain and delve deeper the algorithms of the hybrid consensus to provide the capabilities for our hybrid blockchain technology.

Stochastic interval-based optimal offering model for residential energy management systems by household owners

This paper proposes an optimal bidding strategy for autonomous residential energy management systems. This strategy enables the system to manage its domestic energy production and consumption autonomously, and trade energy with the local market through a novel hybrid interval-stochastic optimization method. This work poses a residential energy management problem which consists of two stages: day-ahead and real-time. The uncertainty in electricity price and PV power generation is modeled by interval-based and stochastic scenarios in the day-ahead and real-time transactions between the smart home and local electricity market. Moreover, the implementation of a battery included to provide energy flexibility in the residential system. In this paper, the smart home acts as a price-taker agent in the local market, and it submits its optimal offering and bidding curves to the local market based on the uncertainties of the system. Finally, the performance of the proposed residential energy management system is evaluated according to the impacts of interval optimistic and flexibility coefficients, optimal bidding strategy, and uncertainty modeling. The evaluation has shown that the proposed optimal offering model is effective in making the home system robust and achieves optimal energy transaction. Thus, the results prove that the proposed optimal offering model for the domestic energy management system is more robust than its non-optimal offering model. Moreover, battery flexibility has a positive effect on the system’s total expected profit. With regarding to the bidding strategy, it is not able to impact the smart home’s behavior (as a consumer or producer) in the day-ahead local electricity market.

Taking omid to the clouds

We describe how we evolve Omid, a transaction processing system for Apache HBase, to power Apache Phoenix, a cloud-grade real-time SQL analytics engine. Omid was originally designed for data processing pipelines at Yahoo, which are, by and large, throughput-oriented monolithic NoSQL applications. Providing a platform to support converged real-time transaction processing and analytics applications - dubbed translytics - introduces new functional and performance requirements. For example, SQL support is key for developer productivity, multi-tenancy is essential for cloud deployment, and latency is cardinal for just-in-time data ingestion and analytics insights. We discuss our efforts to adapt Omid to these new domains, as part of the process of integrating it into Phoenix as the transaction processing backend. A central piece of our work is latency reduction in Omid's protocol, which also improves scalability. Under light load, the new protocol's latency is 4x to 5x smaller than the legacy Omid's, whereas under increased loads it is an order of magnitude faster. We further describe a fast path protocol for single-key transactions, which enables processing them almost as fast as native HBase operations.

The benefits of using artificial intelligence in payment fraud detection: A case study

This paper presents a case study on the use of advanced artificial intelligence (AI) for the detection of payments fraud. The process applies AI within a typical online payment environment to detect fraudulent transactions in real time. The design focuses on an effective supervised learning engine with a data analytics component to support high-performance fraud detection, improving the predictive value of the original data. The design exploits the discriminant properties of customer data by finding hidden patterns. This feature significantly improves fraud detection rate and performance stability compared with a rule-based solution. The developed solution, based on an advanced AI-based technology and platform increased fraud detection rate from 85 per cent to 90 per cent (in terms of number of transaction records) and to 95 per cent in related amount volume (in terms of transaction value), while the alert rate (the percentage of daily transactions investigated manually) was reduced from 40 per cent to 10 per cent. The solution falls under the category of explainable AI because it can explain the rationale behind the decisions.

The role of digital assets in global payments

This paper examines the role of digital ledger technologies (DLT) and their underlying digital assets in the arena of global payments, arguing that digital assets can make such payments faster, less expensive and more efficient. The paper begins by describing the state of cross-border transactions today, highlighting the often piecemeal development of systems that have led to today’s network of walled gardens corresponding to national borders and currencies. It also discusses DLT solutions that are being tested to overcome these limitations. However, this technology alone cannot overcome liquidity issues in local markets — especially emerging markets. Presently, banks and payment providers resolve these issues by prefunding accounts in local currencies on both sides of a transaction, using something called nostro accounts. This is where digital assets can deliver a clear advantage when used in combination with DLT. Acting as a bridge currency, digital assets can create the necessary pools of liquidity and enable direct transactions. The future of cross-border payments will then be realised through the combination of DLT and digital assets. In this way, banks and payment providers can quickly and easily complete affordable, transparent, real-time transactions. This promise is already being tested by early adopters, and is projected to grow exponentially throughout 2018.

Distributed Energy Transaction Pattern and Block Chain Based Architecture Design

Along with the market reform, marketization transaction of distributed energy will have very broad prospects in the future. Block chain technology is known as "the second generation of internet technology", it has features of decentralized, open and transparent, safe and reliable, which can well meet demand of transaction environment of distributed energy. This paper first analyzes future distributed energy transaction patterns and their transaction participants, electricity transaction mean, capital settlement relationship and so on. Secondly, the block chain technology is introduced to reconstruct distributed energy transaction environment, and a kind of block chain technology based distributed energy transaction architecture is proposed. Then new changes are described for introducing of block chain technology in the field of intelligent power purchase contracts, decentralized real-time transactions, automatic trust mechanism, etc. Finally conclusion is given.

Potencijal i stanje razvoja digitalnog bankarstva u Srbiji

Digitization of processes and services is one of the biggest challenges of modern banking. Driven by the principles of profitability and maximizing user experience, banks conduct the implementation of digital products and services, which affects reorganization of resources and all activities of the banks. Rapid changes in clients' behavior and new technological possibilities are directing banks to the goal of ensuring prompt availability of resources, through real time transactions and loan approval. This paper presents a comparative analysis of the digital banking development in the banking sector in Serbia, focusing on availability of products and services that are current on the market, with follow-up on the same type of trends in the region and the European Union. It also discusses the potential and limitations in the application of a digital concept in banking, such as application of digital signature, demographic characteristics, regulation, technological capabilities and general features of the domicile economy.

Adaptive replica consistency policy for Kafka

With the rapid development of the Internet, such as storm, s4, sparkstreaming and other large data real-time computing framework, is widely used in real-time monitoring, real-time recommendation, real-time transaction analysis and other systems for real-time consumption of data streams, Kafka messaging system has been widely deployed. Aiming at the problem that the Kafka cluster needs a lot of network overhead, disk overhead and memory consumption to ensure the reliability of the message, the clustering load is increased, and a replica adaptive synchronization strategy based on the message heat and replica update frequency is proposed. It is proved that the Kafka cluster can guarantee the reliability of the message, and it can significantly reduce the overhead of the resource and improve the throughput of the cluster by using the method of dynamically adjusting the replica synchronization to reduce the system resource consumption while ensuring the reliability of the message. to ensure the system availability and high performance.

English

English

Theory of Land and Efficient Land Markets through Real Estate Exchange

Real Estate Exchange as to widen the scope of market, facilitate easy trading to avoid wide fluctuations in the market. The exchange would also enable price discovery through efficient allocation and distribution of resources. Real Estate exchange is the need of the hour. It would widen the scope of market, introduce greater liquidity, lead to price discovery, efficient allocation and distribution of resources while integrating the real sector with the economic growth and development. We propose an equity and debt participatory note instrument to be devised to distribute the legal and economic right over the real estate. An entire system of institutions and departments is further proposed in the article to enable the real estate exchange to operate and function. Digitalization of land records along with real time transaction recording is also proposed as one of the requisites for setting up an exchange. The exchange proposed aims to achieve allocation, distribution and information efficiency.

A multi-scale optimization framework for electricity market participation

Power grids coordinate a diverse set of energy systems (generators, loads, storage devices) to ensure that supply and demands are matched at multiple timescales (from hours to milliseconds). Such coordination is achieved through hierarchical market transactions. This work presents an optimization framework to evaluate revenue opportunities provided by these multi-scale market hierarchies and to determine optimal participation strategies for individual participants. The proposed framework models day-ahead and real-time transactions of energy, ancillary services, and virtual bidding products provided by independent system operators (ISOs). We apply the framework to a combined heat and power system and a utility-scale battery to determine revenue potential from different market layers and products. Analysis using real price signals for 2015 from the California ISO reveals that 60–90% of the total revenue potential (obtained by participating in all markets) is provided by real-time markets alone (which operate at fast timescales). Our studies also indicate that providing ancillary services (in addition to day-ahead and real-time energy products) increases revenue potential by 40–100%, depending on the physical flexibility of the technology. The proposed framework can be used to identify which market layers and products offer the greatest economic potential for different energy technologies. Our results also highlight that existing techno-economic studies that focus exclusively on day-ahead energy markets (operating at slower timescales) can dramatically undervalue dynamic flexibility.

Frequent pattern mining on stream data using Hadoop CanTree-GTree

The need for knowledge discovery from real-time stream data is continuously increasing nowadays and processing of transactions for mining patterns needs efficient data structures and algorithms. We propose a time-efficient Hadoop CanTree-GTree algorithm, using Apache Hadoop. This algorithm mines the complete frequent item sets (patterns) from real time transactions, by utilizing the sliding window technique. These are used to mine for closed frequent item sets and then, association rules are derived. It makes use of two data structures - CanTree and GTree. The results show that the Hadoop implementation of the algorithm performs 5 times better than in Java.

Scheduling analysis of tasks constrained by TDMA: Application to software radio protocols

The work, presented in this article, aims at performing scheduling analysis of Time Division Multiple Access (TDMA) based communication systems. Products called software radio protocols, developed at Thales Communications & Security, are used as case-studies.Such systems are real-time embedded systems. They are implemented by tasks that are statically allocated on multiple processors. A task may have an execution time, a deadline and a release time that depend on TDMA configuration. The tasks also have dependencies through precedence and shared resources. TDMA-based software radio protocols have architecture characteristics that are not handled by scheduling analysis methods of the literature. A consequence is that existing methods give either optimistic or pessimistic analysis results.We propose a task model called Dependent General Multiframe (DGMF) to capture the specificities of such a system for scheduling analysis. The DGMF task model describes, in particular, the different jobs of a task, and task dependencies. To analyze DGMF tasks, we propose their transformation to real-time transactions and we also propose a new schedulability test for such transactions.The general analysis method is implemented in a tool that can be used by software engineers and architects. Experimental results show that our propositions give less pessimistic schedulability results, compared to fundamental methods. The results are less pessimistic for both randomly generated systems and real case-studies from Thales.These results are important for engineering work, in order to limit the over-dimensioning of resources. Through tooling, we also automated the analysis. These are advantages for engineers in the more and more competitive market of software radios.

Model Driven Engineering for Quality of Service Management

Real-time applications managing a large number of real-time data require the use of Real-time Database Management Systems (RTDBMS) to meet temporal constraints of both real-time data and transactions. However, a RTDBMS has a dynamic workload and may be frequently overloaded since the arrival times and workloads of user transactions are unpredictable. Therefore, Quality of Service management solutions have been proposed to guarantee the stability of RTDBMS even during unpredictable overload periods. While effective, the design and reuse of these solutions is challenging because they are not formally modeled and there is no tool neither a methodology that helps us design such solutions. To address these issues, the authors propose a design framework based on the Model-Driven Engineering approach providing a modeling architecture, a strategic methodology and a software tool to support modeling and reusing such solutions. The framework is implemented and tested for a real Qos management solution.