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C9550-413 exam Dumps Source : IBM Operational Decision Manager Advanced V8.7 Application Development
Test Code : C9550-413
Test Name : IBM Operational Decision Manager Advanced V8.7 Application Development
Vendor Name : IBM
: 68 Real Questions
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Many IT departments have applied utility solutions that go beyond fundamental transaction and analytical processing. These packages comprise models that describe definite information behaviors, and these models consume latest statistics to peer if these patterns of data conduct exist. in that case, operational techniques can use this counsel to make decisions. an excellent instance of here's fraud detection. IT statistics engineers use analytics on old information to assess when fraud came about, code this right into a model, and set up the mannequin as a carrier. Then, any operational device can invoke the mannequin, pass it latest data and receive a model “rating” that represents the probability that a transaction may well be fraudulent.
The conventional term for these new packages is synthetic intelligence (AI). They encompass a mixture of search, optimization and analytics algorithms, statistical analysis strategies and template techniques for ingesting facts, executing these ideas and making the outcomes accessible as services referred to as models. The subset of AI that offers with model creation and implementation is once in a while referred to as computer gaining knowledge of (ML).machine gaining knowledge of and artificial Intelligence
IT departments enforce ML and AI solutions in the broader context of their statistics and processing footprint. here is usually depicted because the following four-layer hierarchy.
Layer 1: The facts.
this accretion includes the records disbursed across the business. It comprises mainframe and distributed records reminiscent of product and income databases, transactional facts and analytical facts within the records warehouse and any big facts functions. It also may encompass client, seller and business enterprise information, possibly at remote sites, and even extends to public information akin to twitter, information feeds and survey effects. one other feasible source of information is server efficiency logs that consist of resource usage historical past.
word that these records exist throughout diverse hardware systems together with on-premises and cloud-based mostly. As such, various records elements can exist in distinctive forms and formats (e.g. textual content, ASCII, EBCDIC, UTF-eight, XML, images, audio clips, and so on.). additionally, at this degree will exist hardware and utility that control the information, together with high-velocity records loaders, data purge and archive approaches, submit-and-subscribe strategies for facts replication, as well as these for commonplace backup and recuperation and catastrophe healing planning.
Layer 2: The Analytics Engines.
in this layer exist a mix of hardware and application that executes enterprise analytics towards the data layer. There are a number of ordinary players in this space. They consist of:
just because the records layer occurs across multiple hardware structures and disbursed websites, so will the analytics engines layer. The primary feature of this residue is to provide an optimized statistics access layer in opposition t the underlying statistics as a carrier for AI and operational applications.
Layer 3: The computer learning Platform.
IT implements computing device discovering software during this layer. It accesses the statistics via one or greater of the analytics engines. it is during this layer that IBM supplies its latest providing, Watson machine studying for z/OS (WMLz). WMLz offers a simple computer researching workflow consisting of here steps:
information scientists recognize that probably the most gold standard merits of computing device learning is to use the outcomes in operational programs; as an instance, having an ML model analyze economic information to assess the probability of fraud. This ability that you're going to achieve optimum performance if you happen to installation ML in the hardware environment the place transaction processing occurs. for a lot of gigantic groups this ability the IBM zServer atmosphere.
Layer four: computer getting to know options.
Now that they have the machine learning platform accessible as ML capabilities, they will create mixed AI/ML options that invoke those capabilities. IBM has a couple of able-made solutions for this accretion, together with right here:
Let’s take a deeper dive into how Watson computer studying on Z (WMLz) works and what capabilities it can provide.
Key performance indications (KPIs). WMLz does not inherently recognize what performance elements are essential to you. despite the fact, once these KPIs are defined (either by a consumer or via enforcing some of the machine gaining knowledge of solutions stated above), WMLz can analyze KPI records to look for correlations. for instance, when one KPI (say, I/O in opposition t a vital database) goes up, a further KPI (say CPU utilization) can also go up as neatly. As one more example, several KPIs can be behaviorally an identical, so WMLz can cluster them as a bunch and perform further evaluation throughout corporations. WMLz can additionally assess KPI baseline behaviors in accordance with time-of-day, time zone of transactions or seasonal exercise.
Anomaly Detection. once correlations are found out, WMLz can seem to be contrary consequences and report them as anomalies. In their I/O example above, an anomaly could be pronounced if I/O in opposition t a critical database multiplied but CPU usage reduced.
pattern recognition. As with many machine learning engines, WMLz will look for patterns among KPIs and facts identifiers. as an example, CPU may additionally increase when processing certain categories of transactions.
KPI prediction. An extension of simple KPI processing, WMLz can use the past behaviors of companies of KPIs to foretell the long run. believe their I/O instance once again. The product might also detect that certain transactions develop into greater numerous all over a selected time length, and these transactions eat drastically extra CPU cycles. The product may additionally then predict future CPU spikes.
Batch workload evaluation. Many IT retail outlets have a big contingent of batch processing that is tightly scheduled and contains job and resource dependencies. Some jobs must wait for his or her predecessors to comprehensive, some use big shared components (equivalent to tape drives or forte hardware) and a few are so resource-intensive that then can't be achieved on the same time. WMLz can analyze the workload information, including resource utilization, and supply innovations for balancing materials or tuning elapsed instances.
MLC can charge sample analysis and cost discount. Some IBM utility license costs are billed month-to-month, and the license volume can also rely on highest CPU utilization during height periods. WMLz can analyze CPU utilization throughout time, seek patterns and make predictions and recommendations for utility license cost discount.Watson machine studying for z/OS — points
IBM’s Watson computer learning for z/OS allows IT its option of construction environments to increase models together with IBM SPSS Modeler. These environments aid data scientists through the use of notebooks, information visualization tools and wizards to speed the building system. a few brief-start utility templates are additionally integrated within the toolset for usual business requirements equivalent to fraud detection, load approval and IT operational analytics. The newest edition of WMLz (version 2.1.0) includes help for Ubuntu Linux on Z, java APIs, simplified Python equipment management and several other features.
interested readers may still reference the hyperlinks under for extra exact technical counsel.
# # #
See all articles by using Lockwood LyonREFERENCES
machine getting to know and synthetic Intelligencehttps://en.wikipedia.org/wiki/Machine_learning
records and AI on IBM Zhttps://www.ibm.com/analytics/z-analytics
the usage of Anaconda with Spark — Anaconda 2.0 documentationhttps://doctors.anaconda.com/anaconda-scale/spark/
Watson computer studying - Overviewhttps://www.ibm.com/cloud/laptop-getting to know
Watson machine discovering - Resourceshttps://www.ibm.com/cloud/computer-getting to know/materials
Q1 revenues fell smartly short of estimates.
foreign money concerns had been smartly well-known here.
situation suggests significance of pink Hat deal.
Shares of know-how big IBM (IBM) are one of Wednesday's losers thus far after the enterprise announced its first-quarter income the day before today afternoon. This become the third straight profits omit for the business, as the true line continues to be a controversy. As this work in development continues, traders are left waiting for the essential red Hat (NYSE:RHT) deal to close later this year.
For the quarter, complete revenues got here in at $18.18 billion, which ignored street estimates by $330 million. This omit is even worse for those who trust that after the outdated quarter's pass over, the street typical got here down via $230 million in between experiences. while IBM did beat with the aid of a penny on the base line, this become the smallest beat going back to late 2017's salary misses.
Now i'm sure the bulls will blame the challenge on a strengthening dollar, however I don't see this as a suitable excuse. any individual that has followed the greenback currency Index ("DCI") knows that the USA dollar became up significantly over the prior-year length. additionally, the DCI's energy considering IBM's previous record won't have been satisfactory to trigger this size of revenue omit, peculiarly for the reason that decreased expectations. additionally, administration outlined on the convention call how the enhanced greenback decreased charges and more suitable other profits gadgets because of hedging benefits.
The biggest issue presently is slowing growth in key cloud areas. Cloud salary increase of 10%, or 12% when adjusting for currencies, became down from 22% (20% ex-currency) from the year-ago length. Cloud as a carrier annual exit run fee become up 10% yr over year, or 15% ex-forex, compared to 25% and 20%, respectively, in Q1 2018. overall, the company's right line changed into down 4.7%, and nevertheless would have been down just about 1% when aside from currencies. IBM isn't in increase mode presently.
despite the fact that the business beat on the bottom line and mentioned margin development, traders are surely extra focused on the salary circumstance. it truly is why the pending purple Hat acquisition is so important, no longer best because it will add about $four billion or so that you can IBM's true line, however it comes with a income boom cost of about 15% presently. Time will inform if the big fee, smartly over $30 billion, may be value the added debt and multi-12 months suspension of IBM's buyback plan.
in the coming weeks, IBM is expected to announce its quarterly dividend, which may potentially be a smaller than regular elevate. The buyback plan has reduced the proportion count nicely, helping lengthy-time period dividend increase, but the enterprise may be conservative with its payout raise given the crimson Hat deal. Given the roughly $7 fall in early Wednesday trading, IBM shares have a latest ahead yield of 4.55%, and that's earlier than any dividend boost. The stock has been a pretty good revenue generator in fresh years, youngsters shares have not performed as well because the ordinary market or know-how friends.
within the end, investors are definitely disillusioned with IBM's quarter, as the big profits pass over has shares down more than 5% in Wednesday trading. whereas currencies may also have damage the stated boom rates, the dollar's energy become a prevalent going in, and estimates did decline in the past few months. IBM's problem at the moment is getting the ordinary correct line to grow, which makes the pending crimson Hat deal much more crucial for the longer term. in all probability traders could be a bit extra excited if they get a dividend lift soon, but for now, IBM's situation isn't exactly brilliant.
Disclosure: I/we haven't any positions in any stocks outlined, and no plans to initiate any positions inside the subsequent 72 hours. I wrote this article myself, and it expresses my very own opinions. i'm not receiving compensation for it (aside from from looking for Alpha). I haven't any enterprise relationship with any enterprise whose inventory is outlined in this article.
extra disclosure: author’s further disclosure: traders are all the time reminded that earlier than making any funding, make sure to do your personal correct due diligence on any name at once or in a roundabout way mentioned listed here. investors should also accept as true with in search of suggestions from a broker or financial adviser before making any investment selections. Any cloth listed here should be regarded common assistance, and never relied on as a proper funding suggestion.
I blogged a little about the tech preview of IBM’s Operational resolution manager – advanced product when i used to be at affect and that i lately obtained a full briefing on the new ODM advanced unencumber.
I blogged a bit concerning the tech preview of IBM’s Operational determination manager – superior product when i used to be at affect and i currently acquired a full briefing on the new ODM superior unencumber.
ODM superior has been below development for approximately 2 years with early entry for purchasers lower back in late 2013 and know-how previews all through 2014. At insight 2014 IBM is formally launching the product as a part of ODM 8.7 with a this autumn normal availability. About 10 shoppers bought engaged with the expertise preview and about 20+ alternatives that are suitable use case are actively seeing that the solution. illustration use instances are alerting income to force proactive interactions with consumers according to interaction patterns, precise-time advertising for online systems, operational monitoring of advanced networks and fraud detection.
ODM superior is part of a new packaging of IBM Operational resolution supervisor. IBM ODM 8.7 could have three packages:
the key difference between ODM superior and ODM commonplace is the focal point on situational, stateful, context-aware selections. where ODM normal is transactional, stateless and request-reply, ODM advanced is designed to be situational, stateful, context-mindful and event-driven. furthermore the ODM superior server considers all the deployed determination domains as a set rather than managing them independently as is typically with ODM regular. The intent of ODM advanced is to “bring proactive decisions in the company second”.
This focal point on proactive decision automation requires context – it must be feasible to compile the capabilities and techniques involved even to convey a coherent context inside which a choice can be made that displays all this context. ODM superior is designed to pull records and pursuits collectively to have in mind how every thing is linked and to try this in actual-time. company suggestions and analytics can be utilized to get perception into what is the best thing to do – the surest choice to make – and then push that decision into the actual-time ambiance by which it is required. IBM sees 4 steps during this:
The server is designed to supply a excessive-performance, scale-out structure using a compute and data grid. This allows varied company common sense containers to be deployed that use enterprise guidelines, analytics and context to make choices. These reply to hobbies dealt with by using inbound gateways and communicate out via a group of outbound gateways. with the aid of distributing common sense out near the context statistics the grid method minimizes facts stream and maximizes performance. Synchronous and asynchronous replication as well as disaster restoration be sure that traits and patterns may also be monitored reliably over extended intervals. logic, information and adventure pattern additions do not require the gadget to be introduced down for updates.
IBM ODM superior is purchasable December 5 and more information is available here. IBM is likely one of the vendors in the choice administration techniques Platform technologies record.
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Apr 18, 2019 (Heraldkeeper via COMTEX) -- New York, April 18, 2019: The IOT in Manufacturing Market is expected to exceed more than US$ 33 Billion by 2024 at a CAGR of 27% in the given forecast period.
The whole world is becoming a type of information system which is interlinked with each and every thing and everything else and each and every thing is connected by one or the other way. The data itself is becoming an asset in this information system as there is huge data flow from every connected device that has a portal. The world is becoming more intelligent by implementing a self-driven auto corrective action program to avoid mistakes and damages. Manufacturing firms are centering towards expanding their business procedures and generation yield with ideal asset use. IoT in manufacturing is gaining importance with the invention of smart devices. advanced data analytics techniques, and expanding need of producers to send IoT empowered sub-systems to build the general effectiveness. Industrial IoT sellers are likewise accomplishing control over numerous heterogonous fabricating forms by incorporating progressed IoT stages and answers for robotize producing operations. Industrial IoT conveys transformational esteem in efficiency and competitiveness through data analytics and real time computing systems and controls the general modern process with high accuracy.
Browse Full Report: https://www.marketresearchengine.com/iot-in-manufacturing
The report covers detailed competitive outlook including the market share and company profiles of the key participants operating in the global market. Key players profiled in the report include PTC (U.S.), Cisco Systems, Inc. (U.S.), General Electric (U.S.), SAP SE (Germany), IBM Corporation (U.S.), Zebra Technologies (U.S.), Siemens AG (Germany), Microsoft Corporation (U.S.), Bosch Software Innovations GmbH (Germany), Rockwell Automation (U.S.), and Huawei (China). Company profile includes assign such as company summary, financial summary, business strategy and planning, SWOT analysis and current developments.
The scope of the report includes a detailed study of global and regional markets on IOT in Manufacturing with the reasons given for variations in the growth of the industry in certain regions.
The IOT in Manufacturing Market has been segmented as below:
The IOT in Manufacturing Market is Segmented on the lines of Vertical Analysis, Software Analysis, Services Analysis, Platform Analysis, Application Analysis and Regional Analysis. By Vertical Analysis this market is segmented on the basis of Automotive Manufacturing, Industrial Equipment Manufacturing, Electronics and Communication Equipment Manufacturing, Chemicals and Materials Equipment Manufacturing, Food and Agriculture Equipment Manufacturing and Others. By Software Analysis this market is segmented on the basis of Network and Application Security, Network and Bandwidth Security, Remote Monitoring System, Data Management and Smart Surveillance. By Services Analysis this market is segmented on the basis of Managed Services and Professional Services.
By Platform Analysis this market is segmented on the basis of Device Management Platform, Application Management Platform and Connectivity Management Platform. By Application Analysis this market is segmented on the basis of Predictive Maintenance, Business Process Optimization, Asset Tracking and Management, Logistics and Supply Chain Management, Real-Time Workforce Tracking and Management, Automation Control and Management and Emergency & Incident Management and Business Communication. By Regional Analysis this market is segmented on the basis of North America, Europe, Asia-Pacific and Rest of the World.
This report provides:
1) An overview of the global market for IOT in Manufacturing and related technologies.2) Analyses of global market trends, with data from 2015, estimates for 2016 and 2017, and projections of compound annual growth rates (CAGRs) through 2024.3) Identifications of new market opportunities and targeted promotional plans for IOT in Manufacturing.4) Discussion of research and development, and the demand for new products and new applications.
5) Comprehensive company profiles of major players in the industry.
The restraining factors of IOT in Manufacturing are as follows:
The major driving factors of IOT in Manufacturing are as follows:
Request Sample Report from here: https://www.marketresearchengine.com/iot-in-manufacturing
The IOT in Manufacturing is segmented on the lines of its Software, service, platform, and application, vertical and regional. Based on software segmentation it covers network and application security, network and bandwidth security, remote monitoring system, data management, smart surveillance. Based on service segmentation it covers managed, professional service. Based on platform segmentation it covers device management platform, application management platform, connectivity management platform. Based on application segmentation it covers predictive maintenance, business process optimization, asset tracking and management, logistics and supply chain management, real-time workforce tracking and management, automation control and management, emergency & incident management and business communication. Based on vertical segmentation it covers automotive manufacturing, industrial equipment manufacturing, electronics and communication equipment manufacturing, chemicals and materials equipment manufacturing, food and agriculture equipment manufacturing, others. The IOT in Manufacturing on geographic segmentation covers various regions such as North America, Europe, Asia Pacific, Latin America, Middle East and Africa. Each geographic market is further segmented to provide market revenue for select countries such as the U.S., Canada, U.K. Germany, China, Japan, India, Brazil, and GCC countries.
Reasons to Buy this Report:
1) Obtain the most up to date information available on all IOT in Manufacturing.2) Identify growth segments and opportunities in the industry.3) Facilitate decision making on the basis of strong historic and forecast of IOT in Manufacturing Market data.4) Assess your competitor's refining portfolio and its evolution.
Other Related Market Research Reports:
IOT in Testing Market Worth US$ 13.5 Billion by 2022
IOT in Discreet Manufacturing Market to Cross US$ 49 Billion by 2022
Company Name: Market Research Engine
Contact Person: John Bay
Country: United States
The financial services sector thinks quantum computing can help supercharge its products and profits, but what impact could it have on cyber-risk exposure?
Opinion is divided over when quantum computers will enter the commercial mainstream: IBM reckons four to five years; more conservative forecasts suggest not for a decade, at least. Whichever timeframe proves correct, it’s probable that the financial services sector will be foremost to adopt quantum tech for line-of-business applications – and to bear the brunt of the challenges that early adopters always face.
Quantum promises pros but also cons, especially in respect to the cyber-security liabilities that its power gives rise to. Quantum computing will “redefine” banking, says Elisabetta Zaccaria, chairman at Secure Chorus, an organisation that promotes multi-stakeholder cooperation in information security. “Yet while quantum computers present an opportunity to solve many challenges, they also create problems for data security,” she says.
Analysts do largely agree, however, about the prospective market opportunities. According to BCC Research’s Quantum Computing: Technologies and Global Markets, its expansion into key industries will boost demand for quantum-scale solutions. The industry anticipates a compound annual growth rate (CAGR) of 37.3 per cent to 2022, when it could be worth $161m; this date falls before the earliest estimate for mainstream penetration to begin. The report further estimates that between 2022 and 2027, the market will see a CAGR approaching 53 per cent and be worth $1.3bn. By sector application, the financial services will see a CAGR from 2022-2027 of 62.6 per cent.
Such profitability is likely to derive from a quantum computing industry formed around similar lines to IT and computing markets – hardware, software and services. The hardware has already started to arrive. IBM consolidated its prediction at the start of 2019 with the launch of its Q System One, a standalone platform running at 20 qubits that claims to be the first integrated general-purpose quantum computing system designed for commercial use.
Start-ups like D-Wave and Rigetti Systems, meanwhile, have been marketing more task-specific machines. D-Wave sells self-contained quantum computers with a variant form of quantum technology known as quantum annealing. There are also incumbent technology leaders like Fujitsu, whose Digital Annealer offering is based on “quantum-inspired” architecture.
IBM points to more supportive evidence that quantum will arrive sooner rather than later: global engagement with its Q Network, an initiative set up to advance a quantum computing development framework by providing cloud-based access to quantum compute resources and simulators for like-minded parties to try their own ideas in an ‘approximate’ quantum environment. Banks JPMorgan Chase and Barclays are two of the biggest commercial members of Q Network. They are engaged upon a range of development projects designed to determine which of their financial services operations are most likely to benefit from being scaled to quantum environments.
Their projects are described as exploratory but, from their public positioning statements, it’s clear that both financial institutions plan to make quantum integral to their commercial strategies, as well as their IT agendas, for the 2020s. JPMorgan Chase’s senior engineer and quantum project lead Constantin Gonciulea is investigating a limited range of financial services areas where quantifiable operational benefit could be derived from a phased transition to quantum enablement.
Areas under investigation include the redefinition of trading strategies, optimisation of portfolio management, asset pricing and risk analysis. “Fund managers have to choose among an infinite number of investment combinations for their portfolios, based on the level of risk they want to take,” Gonciulea explains. “Imagine if they had a computer that could detect potential risk – based on their appetite for risk – within seconds of the change taking place in a particular market.”
On the information security front, enhanced risk-detection capability could also be applied to fraud. Gonciulea adds: “JPMorgan Chase has thousands of employees who detect fraud as part of their job. The application of quantum computing to the assessment of potential fraudulent payments… would be very beneficial for their merchant services clients.”
The IT security industry itself has been reviewing quantum’s potential to enhance its own value proposition for some time, both as a counteractive to cyber threats – to conduct cyber-risk analysis – and to drive better enterprise threat intelligence.
Ridgeback Network Defense, for example, is developing algorithms that can leverage quantum systems to evaluate risk within an organisation’s IT infrastructure and identify possible changes in system state, based on different attack or fault scenarios. “This will allow a financial organisation to optimise the allocation of resources and provide an organisation with a near real-time analysis of likely what-if scenarios for both information security and for operations,” says the company’s CTO Thomas Phillips. “The same technology should, by the way, be applicable also to market analysis.”
It has also been recognised that as quantum compute capability becomes available for mainstream commerce, it also falls within reach of malevolent nation states and even cyber criminals. Indeed, according to Frederik Kerling, senior quantum expert at Atos, the biggest impact on financial services will not be the deployment of quantum computers; it will be preparing for the cyber criminals to have quantum computers. But “quantum vulnerabilities [can] be countered by quantum solutions”, he suggests.
According to Atos’s 2018 report Quantum Computing in Financial Services, as well as the desire to attain operational efficiencies and superior decision-making, there’s another driving factor for financial services firms’ take-up of quantum: to make it integral to their digital transformation programmes as they move away from aged platforms that, in an era of cryptocurrencies and blockchain, now embody unacceptable degrees of risk exposure.
Cryptocurrencies such as Bitcoin are likely to be a target for quantum computing with its ability to break the underlying security technology behind them. Current encryption standards, therefore, will be particularly vulnerable to quantum-powered attacks, which makes cyber security a critical aspect of quantum computing.
StandardBanking on security by decree For cyber security, there are encouraging signs of growing recognition of the need to secure quantum. In 2018, the Accredited Standards Committee X9 established a study group to review the risk posed to the financial services industry by quantum computing. The group will review the status of quantum computing and assess the effects large-scale, fault-tolerant, general-purpose quantum computers will have on the cryptography used by the industry. The group will also review efforts by companies and government agencies to identify both at-risk and quantum-safe algorithms.
“Several banks are performing quantum risk assessments, implementing proofs of concept, and looking at deploying quantum-resistant algorithms in select systems,” says the Global Risk Institute’s Michele Mosca. “For years, some have also used quantum cryptography in their link encryptors to protect the link to their off-site backups.”
Secure Chorus is collaborating with ISARA Corporation to evolve a cryptography standard called MIKEY-SAKKE (Multimedia Internet KEYing-Sakai-Kasahara Key Encryption) to become quantum-safe. Developed by the UK’s National Technical Authority for Information Assurance, now part of the National Cyber Security Centre, MIKEY-SAKKE was standardised by the Internet Engineering Task Force.
In addition to encryption and cyber security, elsewhere quantum computing will have impact throughout the financial services industry, from retail-banking customer relationship management (CRM) and investment portfolio risk analysis, to high-frequency trading (HFT) and capital markets.
Analytics-driven CRM is a hot topic among financial services players, for both incumbents and fintech arrivistes. In retail banking and insurance, CRM will be improved by the automation of more ‘tightly targeted’ services. Atos suggests that quantum technology can provide “greater accuracy in simulating customer purchasing preferences based on demographic data, whether that’s for an insurance policy or a mortgage. Customer personal information, meanwhile, can be protected more effectively through simultaneous automation and analytics of pending threats.”
In HFT, ultra-fast computers and algorithms on advanced, highly specified trading platforms can execute thousands of trades per second at nanosecond speeds. According to TABB Group, HFT accounts for around 50 per cent of all US equity trades – even though some analysts and traders attribute to HFT spates of stock market volatility that date back to the ‘Flash Crash’ incident of 2010 (see E&T Vol 5, Issue 13 and Vol 8, Issue 6). Quantum computing will accelerate HFT trading further yet and enhance it with artificial intelligence and predictive capabilities.
Risk analysis is “the most promising – and exciting – application of quantum computing at this time. Practical quantum algorithms will be developed to evaluate risk much more accurately and rapidly,” says Phillips at Ridgeback Network Defense. “A paper published in the journal Physical Review A showed how a quantum system could dramatically speed up the Monto Carlo algorithm for pricing financial derivatives.” A Monte Carlo algorithm is a computer algorithm used to simulate the behaviour of other systems. It is not an exact method, but a heuristic one, typically using randomness and statistics to obtain results.
Before it can exploit these good things, the financial industry will have to resolve the questions of information security and data integrity that come in tandem with the benefits of ‘quantumisation’. As noted, along with driving supercharged growth, quantum could power a new generation of advanced persistent threats, according to the Global Risk Institute.
The threats stem from the power of quantum computing to execute tasks far beyond the reach of conventional computers. These use long strings of bits that encode either a 0 or a 1. By contrast, quantum computing enables the bit to embody the 0 and 1 states simultaneously. By manipulating a large collection of quantum bits – qubits – quantum computers can process countless configurations of 0s and 1s at the same time.
“Quantum computers pose a systemic threat to cyber security, as they will break the current public-key cryptography that underpins the security of most technology platforms,” warns Michele Mosca, special advisor on cyber security at the Global Risk Institute. “The integrity of software validation, identity verification, and information encryption will all be affected.”
Successful attacks could result in a lack of confidence and trust in the tools and institutions underpinning their digital economy dependent on quantum-vulnerable cryptography for security, Mosca adds: “Financial institutions depend on the strength of their reputation and the trust placed in them by individuals and organisations to keep their assets safe and their transactions secure. The emergence of quantum computers will challenge this trust, especially since it will be difficult to evaluate the extent of the threat before an attack happens.”
Further warning also comes from Zaccaria at Secure Chorus. “Quantum computing will solve complex mathematical problems faster and better – including those used at the core of modern cryptography,” she says. “Moreover, quantum computers deployed by malicious attackers will be able to decrypt the data protected by many public key cryptography methods now used by governments and corporations to protect sensitive data. And they will be able to do this relatively quickly.”
Concerns about crypto-key exchange integrity are growing through the fintech sector and will have to be addressed if quantum growth is to proceed as forecast, says Zaccaria. To this end, Secure Chorus is steering a quantum-safe initiative in public key cryptography with the development of a new set of public key cryptosystems for conventional computers that resist quantum computer attack. These cryptosystems are called ‘quantum-safe’ or ‘post-quantum cryptography’. The underlying principle is the use of mathematical problems of a complexity beyond quantum computing’s capability to solve them.
“The wallets protecting crypto-assets are generally vulnerable to quantum attacks, although the consensus mechanisms based on proof-of-work – e.g. Bitcoin, Ethereum, and suchlike – seem less vulnerable,” says Mosca at the Global Risk Institute. “Nevertheless, asymmetric distribution of full-scale quantum computing platforms could pose a threat to the network that should be taken into account when designing quantum-resistant blockchains.”
Such defensive initiatives notwithstanding, Ridgeback Network Defense’s Phillips expects standard due diligence to kick in long before risk exposure reaches critical levels. “If companies choose to depend on blockchain technology – public or private implementations – then I expect them to stay abreast of weaknesses. Quantum technology will march along at a steady pace. There will be fair warning before they see any quantum advances that could threaten existing cryptographic implementations used by financial institutions.”
Jacky Fox, director of cyber risk at Deloitte Ireland, agrees that concerns about the level of quantum cyber-risk should not be over-estimated – for the time being. “Controlled quantum, as opposed to virtual qubits [quantum binary digits], are currently being measured by the tens rather than the thousands or millions required for cyber-threat activity by any actor,” she explains. “Quantum-computing-as-a-Service – QCaaS – will be a likely delivery model with single- and multi-tenancy offerings.”
Fox continues: “How could these services be restricted from criminals? Should commercial organisations be regulated to vet and monitor their clients for criminal cyber-threat activity? State-sponsored activity against financial services seems more probable but, given some of the current challenges in relation to bandwidth, error correction and environmental requirements for quantum computing, 2025 sounds ambitious to have scale general-purpose quantum solutions available to anyone.”
Case studyNatWest accelerates understanding of risk Financial institutions face an ongoing challenge with the creation and maintenance of optimally balanced portfolios of assets, selected from thousands of options. Ideally, these incorporate various liquid assets that deliver the maximum possible return while helping maintain risk at an acceptable level. While liquidity is of critical importance to financial institutions, the process involved in the calculation of the best combination of assets – an expensive, time-consuming manual task – is generally infrequently undertaken.
Retail bank NatWest has partnered with Fujitsu on a proof-of-concept project that aims to optimise its mix of high-quality liquid assets, including bonds, cash and government securities. Fujitsu’s Digital Annealer compute platform processes this type of complex scenario and provides results “orders of magnitude faster” than conventional compute resources can deliver, NatWest reports.
NatWest’s quantum technology team has completed highly complex calculations on the bank’s £120bn-value High Quality Liquid Assets portfolio at 300 times the speed of conventional cloud-based compute resources, says director of innovation Kevin Hanley, and with a higher degree of accuracy.
“NatWest can complete a comprehensive risk assessment for its portfolio much faster, as well as gaining access to a far wider range of results and permutations, therefore helping to ensure an optimised spread and reduced risk,” explains Hanley. “The technology could be applied to other calculations and [risk-based] problems the bank faces on a daily basis.”Quantum timeline
1927: German physicist Werner Heisenberg introduces the ‘uncertainty principle’, which says that you can’t know everything about a quantum mechanical particle.
1935: Albert Einstein, Boris Podolsky and Nathan Rosen argue that a complete theory must be local and realistic. No particle should affect another faster than the speed of light, and every measurable quantity of nature has to be accurately represented by theory. The ‘EPR paradox’ says that either quantum mechanics is incomplete or the requirements for a complete theory are too strong.
1964: John Bell formulates a local hidden variable model and shows the bound on how strongly things can be correlated. Quantum mechanics can violate this bound and has measurement outcomes with greater correlation than any local hidden theory. Quantum mechanics allows for stronger correlations with entangled states.
1970: Notes from Stephen Wiesner and Charles Bennett contain the possible first use of the phrase ‘quantum information theory’ and the first idea of entanglement as a communication resource.
1981: Nobel Prize winner Richard Feynman challenges a group of computer scientists to develop a new breed of computer based on quantum physics. Almost 40 years later, scientists around the world are getting closer to this.
1994: MIT’s Peter Shor shows you can factor a number into its primes on a quantum computer – a problem that takes classical computers “an exponentially long time”. His algorithm launches an explosion of interest in the field of quantum computing.
1995: Quantum error correction emerges, showing that it’s possible to use a subtle redundancy to protect against environmental noise, making the physical realisation of quantum computing more tenable.
1996: IBM’s David DiVincenzo’s five requirements for a quantum computer: a well-defined scalable qubit array; an ability to initialise the state of the qubits to a simple fiducial state; a ‘universal’ set of quantum gates; long coherence times, much longer than the gate-operation time; single-qubit measurement.
1997: The first topological quantum error connecting code – the surface code – is proposed by California Institute of Technology’s Alexei Kitaev. The surface code is considered the most promising platform for realising a scalable, fault-tolerant quantum computer.
2001: Shor’s algorithm is demonstrated in a quantum computing experiment, albeit with a very pedestrian problem: 15 = 3x5. The IBM system employed qubits in nuclear spins, similar to an MRI machine.
2004: Robert Schoelkopf and his collaborators at Yale University invent circuit QED to study the interaction of a photon and an artificial quantum object on a chip. This established the standard for coupling and reading out superconducting qubits.
2007: Schoelkopf and his collaborators invent a type of superconducting qubit with reduced sensitivity to charge noise, a major obstacle for long coherence. The superconducting qubit has been adopted by many superconducting quantum groups.
2012: Several important parameters for quantum information processing with transmon qubits are improved. IBM extends the coherence time, the duration that a qubit retains its quantum state, up to 100 microseconds.
2015: IBM demonstrates the smallest, almost-quantum error detection code. With a single quantum state stabilised, it’s possible to detect bit-flips and phase-flips. The code is in a 4-qubit lattice arrangement, a building block for quantum computing systems.
2016: IBM scientists build the IBM Q experience, a quantum computing platform delivered via the IBM cloud. It enables users to run experiments on IBM’s quantum processor, work with individual qubits, and explore tutorials and simulations of quantum computing.
SAN JOSE, Calif., June 13, 2017 /PRNewswire/ -- (DataWorks Summit/Hadoop Summit) -- IBM (NYSE: IBM) and Hortonworks (NASDAQ: HDP) today announced an expansion to their relationship focused on extending data science and machine learning to more developers and across the Apache Hadoop ecosystem. The companies are combining Hortonworks Data Platform (HDP®) with IBM Data Science Experience and IBM Big SQL into new integrated solutions designed to help everyone from data scientists to business leaders better analyze and manage their mounting data volumes and accelerate data-driven decision-making.
"The combination of IBM's data science and Hortonworks' open and connected data platforms will benefit not only their respective clients, but also the Apache open source community because of their combined investment and collaboration," said Rob Bearden, CEO, Hortonworks. "We're excited about the inevitable acceleration in technical innovations that this relationship is being designed to foster, the result being smarter and more agile businesses."
"This partnership will provide an integrated and open data science and machine learning platform that lets teams easily collaborate and operationalize data science," said Rob Thomas, General Manager, IBM Analytics. "Incorporating advanced machine learning and deep learning capabilities, the combination of Hortonworks Data Platform with IBM's Data Science Experience and the IBM Machine Learning platform can help clients achieve improved analytic results faster and at scale."
The news builds on the long-standing relationship between the companies and includes the following:
IBM Data Science Experience provides a set of critical tools and a collaborative environment through which analysts and developers can create new analytic models quickly and easily. For example, IBM Machine Learning, found in the Data Science Experience, can speed the time it takes to build and deploy analytic models for application development by two-times, according to IBM testing.
In addition to the companies' numerous product collaborations, IBM and Hortonworks are also founding members of the Open Data Platform Initiative (ODPi). Launched in February 2015, ODPi is comprised of industry leaders working collaboratively to define and promote a set of standard open source technologies and increase compatibility among big data platforms.
The expanded partnership also builds on an existing partnership and joint solutions including HortonWorks Data Platform (HDP) for IBM's Power Systems and Spectrum Scale Storage. Customers can benefit from fast access to data and a cost-effective platform for running their big data and cognitive workloads.
Earlier this week Hortonworks announced Hortonworks DataFlow (HDFTM) for IBM Power Systems. HDF, the industry's only data ingest, stream processing and streaming analytics platform built entirely on open source software, is designed to enable customers to collect, curate, analyze and act on all data in real-time, across the data center and cloud. Combined with IBM Power Systems, customers can gain access to industry-leading performance and efficiency for streaming analytics. HDF is complementary to HDP and is designed to accelerate the flow of data in motion into HDP to support full fidelity analytics.
Partnering On Apache As part of their wide-ranging partnership, the companies will also team to advance the development of Unified Governance (IBM BigIntegrate, IBM BigQuality and IBM Information Governance Catalog) on the Apache Atlas open platform. Atlas provides a scalable governance platform for Enterprise Hadoop which is designed to help developers model new business processes and data assets quickly and easily. Through their work, both companies plan to help advance Atlas from its current Incubator status to Apache Top Level Project status, where projects are typically released for open development and deployment.
In addition to Atlas, the companies will also partner on the advancement of Apache Spark, the open source framework for processing and analyzing large data sets across clustered environments. The companies will also collaborate to advance the Apache Hadoop framework itself, working to unify access to multi-vendor, heterogeneous data environments across data warehouses and databases – ultimately aiming to simplify the environment for better value from all data.
IBM – A Leader in Gartner Data Science Magic Quadrant IBM was recently named a Leader in the February 2017 Gartner Magic Quadrant for Data Science Platforms. The report, which evaluated data science platforms from 16 analytics vendors, describes data science platforms as "products that organizations use to build machine-learning solutions themselves, as opposed to outsourcing their creation or buying ready-made solutions."
About Hortonworks Hortonworks is an industry-leading innovator that creates, distributes and supports enterprise-ready open data platforms and modern data applications that deliver actionable intelligence from all data: data-in-motion and data-at-rest. Hortonworks is focused on driving innovation in open source communities such as Apache Hadoop, Apache NiFi and Apache Spark. Along with its 2,100+ partners, Hortonworks provides the expertise, training and services that allow customers to unlock transformational value for their organizations across any line of business.
Hortonworks, HDP and HDF are registered trademarks or trademarks of Hortonworks, Inc. and its subsidiaries in the United States and other jurisdictions. All other trademarks are the property of their respective owners. For more information, please visit www.hortonworks.com.
About IBM For more about IBM Analytics visit www.ibm.com/analytics.
Contact Mike ZimmermanIBM Media Relations email@example.com (585) 698-9974
Michelle Lazzar Hortonworks Media Relations firstname.lastname@example.org (408) 828-9681
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