Blockchain Now and Tomorrow Assessing Multidimensional Impacts of Distributed Ledger Technologies
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Blockchain & Distributed Ledger Technologies
Science, Technology Assessment, SEPTEMBER 2019 and Analytics WHY THIS MATTERS Distributed ledger technology (e.g. blockchain) allows users to carry out digital transactions without the need SCIENCE & TECH SPOTLIGHT: for a centralized authority. It could fundamentally change the way government and industry conduct BLOCKCHAIN & DISTRIBUTED business, but questions remain about how to mitigate fraud, money laundering, and excessive energy use. LEDGER TECHNOLOGIES /// THE TECHNOLOGY What is it? Distributed ledger technologies (DLT) like blockchain are a secure way of conducting and recording transfers of digital assets without the need for a central authority. DLT is “distributed” because multiple participants in a computer network (individuals, businesses, etc.), share and synchronize copies of the ledger. New transactions are added in a manner that is cryptographically secured, permanent, and visible to all participants in near real time. Figure 2. How blockchain, a form of distributed ledger technology, acts as a means of payment for cryptocurrencies. Cryptocurrencies like Bitcoin are a digital representation of value and represent the best-known use case for DLT. The regulatory and legal frameworks surrounding cryptocurrencies remain fragmented across Figure 1. Difference between centralized and distributed ledgers. countries, with some implicitly or explicitly banning them, and others allowing them. How does it work? Distributed ledgers do not need a central, trusted authority because as transactions are added, they are verified using what In addition to cryptocurrencies, there are a number of other efforts is known as a consensus protocol. Blockchain, for example, ensures the underway to make use of DLT. For example, Hyperledger Fabric is ledger is valid because each “block” of transactions is cryptographically a permissioned and private blockchain framework created by the linked to the previous block so that any change would alert all other Hyperledger consortium to help develop DLT for a variety of business users. -
Rdbmss Why Use an RDBMS
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Liquidity Or Leakage Plumbing Problems with Cryptocurrencies
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Microsoft Power BI DAX
Course Outline Microsoft Power BI DAX Duration: 1 day This course will cover the ability to use Data Analysis Expressions (DAX) language to perform powerful data manipulations within Power BI Desktop. On our Microsoft Power BI course, you will learn how easy is to import data into the Power BI Desktop and create powerful and dynamic visuals that bring your data to life. But what if you need to create your own calculated columns and measures; to have complete control over the calculations you need to perform on your data? DAX formulas provide this capability and many other important capabilities as well. Learning how to create effective DAX formulas will help you get the most out of your data. When you get the information you need, you can begin to solve real business problems that affect your bottom line. This is Business Intelligence, and DAX will help you get there. To get the most out of this course You should be a competent Microsoft Excel user. You don’t need any experience of using DAX but we recommend that you attend our 2-day Microsoft Power BI course prior to taking this course. What you will learn:- Importing Your Data and Creating the Data Model CALCULATE Function Overview of importing data into the Power BI Desktop Exploring the importance of the CALCULATE function. and creating the Data Model. Using complex filters within CALCULATE using FILTER. Using ALLSELECTED Function. Using DAX Syntax used by DAX. Time Intelligence Functions Understanding DAX Data Types. Why Time Intelligence Functions? Creating a Date Table. Creating Calculated Columns Finding Month to Date, Year To Date, Previous Month How to use DAX expressions in Calculated Columns. -
The Economics of Distributed Ledger Technology for Securities Settlement
ISSN 2379-5980 (online) DOI 10.5195/LEDGER.2019.144 RESEARCH ARTICLE The Economics of Distributed Ledger Technology for Securities Settlement Evangelos Benos,*† Rodney Garratt,‡ Pedro Gurrola-Perez§ Abstract. We apply economic principles to understand how distributed ledger technology (DLT) might impact the innovation process and eventual market structure in the security settlement industry. Our main conclusions are that: i) Although DLT has the potential to significantly reduce costs in securities settlement, implementation is challenging, ii) technological innovation in the post-trade industry is more likely to succeed with some degree of coordination, which could be facilitated by the relevant authorities, and iii) DLT- based securities settlement is likely to be concentrated among few providers which, absent any regulation, could result in inefficient monopoly pricing or efficient price discrimination with service providers capturing much of the market surplus. 1. Introduction Over the last few years, a number of technological innovations with potentially transformative implications for the financial system have been receiving close attention from researchers, industry participants, technology firms, and regulators. In particular, for the post-trade cycle, which includes clearing and settlement of financial transactions, the distributed ledger technology (or DLT) is considered to be promising, with the potential to drastically simplify processes, reduce costs, and increase efficiency and security. This has spurred a flurry of analysis -
Anti-Money Laundering and Blockchain Technology
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Blockchain Ready Manufacturing Supply Chain Using Distributed Ledger
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The Seconomics (Security-Economics) Vulnerabilities of Decentralized Autonomous Organizations?
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Column-Stores Vs. Row-Stores: How Different Are They Really?
Column-Stores vs. Row-Stores: How Different Are They Really? Daniel J. Abadi Samuel R. Madden Nabil Hachem Yale University MIT AvantGarde Consulting, LLC New Haven, CT, USA Cambridge, MA, USA Shrewsbury, MA, USA [email protected] [email protected] [email protected] ABSTRACT General Terms There has been a significant amount of excitement and recent work Experimentation, Performance, Measurement on column-oriented database systems (“column-stores”). These database systems have been shown to perform more than an or- Keywords der of magnitude better than traditional row-oriented database sys- tems (“row-stores”) on analytical workloads such as those found in C-Store, column-store, column-oriented DBMS, invisible join, com- data warehouses, decision support, and business intelligence appli- pression, tuple reconstruction, tuple materialization. cations. The elevator pitch behind this performance difference is straightforward: column-stores are more I/O efficient for read-only 1. INTRODUCTION queries since they only have to read from disk (or from memory) Recent years have seen the introduction of a number of column- those attributes accessed by a query. oriented database systems, including MonetDB [9, 10] and C-Store [22]. This simplistic view leads to the assumption that one can ob- The authors of these systems claim that their approach offers order- tain the performance benefits of a column-store using a row-store: of-magnitude gains on certain workloads, particularly on read-intensive either by vertically partitioning the schema, or by indexing every analytical processing workloads, such as those encountered in data column so that columns can be accessed independently. In this pa- warehouses.