DBs, DWs are merging as In-memory DBs: SAP® In-Memory Computing Enabling Real-Time Computing SAP® In-Memory enables real-time computing by bringing together online transaction proc. OLTP (DB) and online analytical proc. OLAP (DW). Combining advances in hardware technology with SAP InMemory Computing empowers business – from shop floor to boardroom – by giving real-time bus. proc. instantaneous access to data-eliminating today’s info lag for your business. In-memory computing is already under way. The question isn’t if this revolution will impact businesses but when/ how. In-memory computing won’t be introduced because a co. can afford the technology. It will be because a business cannot afford to allow its competitors to adopt the it first. Here is sample of what in-memory computing can do for you: • Enable mixed workloads of analytics, operations, and performance management in a single software landscape. • Support smarter business decisions by providing increased visibility of very large volumes of business information • Enable users to react to business events more quickly through real-time analysis and reporting of operational data. • Deliver innovative real-time analysis and reporting. • Streamline IT landscape and reduce total cost of ownership. Product managers will still look at inventory and point-of-sale data, but in the future they will also receive,eg., tell customers broadcast dissatisfaction with a product over Twitter. Or they might be alerted to a negative product review released online that highlights some unpleasant product features requiring immediate action. From the other side, small businesses running real-time inventory reports will be able to announce to their Facebook and Twitter communities that a high demand product is available, how to order, and where to pick up. Bad movies have been able to enjoy a great opening weekend before crashing 2nd weekend when negative word-of-mouth feedback cools enthusiasm. That week-long grace period is about to disappear for silver screen flops. Consumer feedback won’t take a week, a day, or an hour. The very second showing of a movie could suffer from a noticeable falloff in attendance due to consumer criticism piped instantaneously through the new technologies. It will no longer be good enough to have weekend numbers ready for executives on Monday morning. Executives will run their own reports on revenue, Twitter their reviews, and by Monday morning have acted on their decisions. The final example is from the utilities industry: The most expensive energy a utilities provides is energy to meet unexpected demand during peak periods of consumption. If the company could analyze trends in power consumption based on real-time meter reads, it could offer – in real time – extra low rates for the week or month if they reduce their consumption during the following few hours. In manufacturing enterprises, in-memory computing tech will connect the shop floor to the boardroom, and the shop floor associate will have instant access to the same data as the board [[shop floor = daily transaction processing. Boardroom = executive data mining]]. The shop floor will then see the results of their actions reflected immediately in the relevant Key Performance Indicators (KPI). This advantage will become much more dramatic when we switch to electric cars; predictably, those cars are recharged the minute the owners return home from work. Hardware: blade servers and multicore CPUs and memory capacities measured in terabytes. Software: in-memory database with highly compressible row / column storage designed to maximize in-memory comp. tech. SAP BusinessObjects Event Insight software is key. In what used to be called exception reporting, the software deals with huge amounts of realtime data to determine immediate and appropriate action for a real-time situation. [[Both row and column storage! They convert to column-wise storage only for Long-Lived-High-Value data?]] Parallel processing takes place in the database layer rather than in the app layer - as it does in the client-server arch. Total cost is 30% lower than traditional RDBMSs due to: • Leaner hardware, less system capacity req., as mixed workloads of analytics, operations, performance mgmt is in a single system, which also reduces redundant data storage. [[Back to a single DB rather than a DB for TP and a DW for boardroom dec. sup.]] • Less extract transform load (ETL) between systems and fewer prebuilt reports, reducing support required to run sofwr. Report runtime improvements of up to 1000 times. Compression rates of up to a 10 times. Performance improvements expected even higher in SAP apps natively developed for inmemory DBs. Initial results: a reduction of computing time from hours to seconds. However, in-memory computing will not eliminate the need for data warehousing. Real-time reporting will solve old challenges and create new opportunities, but new challenges will arise. SAP HANA 1.0 software supports realtime database access to data from the SAP apps that support OLTP. Formerly, operational reporting functionality was transferred from OLTP applications to a data warehouse. With in-memory computing technology, this functionality is integrated back into the transaction system. Adopting in-memory computing results in an uncluttered arch based on a few, tightly aligned core systems enabled by service-oriented architecture (SOA) to provide harmonized, valid metadata and master data across business processes. Some of the most salient shifts and trends in future enterprise architectures will be: • A shift to BI self-service apps like data exploration, instead of static report solutions. • Central metadata and masterdata repositories that define the data architecture, allowing data stewards to work across all business units and all platforms Real-time in-memory computing technology will cause a decline Structured Query Language (SQL) satellite databases. The purpose of those databases as flexible, ad hoc, more business-oriented, less IT-static tools might still be required, but their offline status will be a disadvantage and will delay data updates. Some might argue that satellite systems with in-memory computing technology will take over from satellite SQL DBs. SAP Business Explorer tools that use in-memory computing technology represent a paradigm shift. Instead of waiting for IT to work on a long queue of support tickets to create new reports, business users can explore large data sets and define reports on the fly.
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Sistema de selecció d’atributs basat en algorismes genètics Resultats pràctics (I) Taula de resultats Sense Selecció Alg. Genètics B+b Relief N.Atrib. % encerts N. Atrib. % encerts N.Atrib % encerts N.Atrib. % encerts AUD 69 64,78±9,42 35,55±2,5 74,42±6,52 21,20±1,32 54,73±10,65 8,90±2,02 29,25±9,34 AUS 14 67,52±4,67 5,03±0,65 83,14±3,99 2,90±0,32 58,48±8,32 2,70±0,67 70,89±6,58 BAL 4 80,78±4,73 4 80,78±4,73 3 69,10±2,96 1,90±0,32 58±8,37 BPA 6 63,54±7,62 5,53±0,9 63,31±7,46 2,90±0,32 62,13±11,28 1 54,99±13,08 6,10±0,32 68,60±10,50 GLS 9 68,26±8,13 5,1±1,15 60,65±12,64 2 49,21±6,05 H-C 13 63,13±7,96 5,95±0,71 79,54±5,12 3,90±0,74 64,65±9,42 * HEP 19 68,65±10,46 8,60±0,94 77,47±6,38 2,90±0,32 69,69±18,04 * ION 34 85,51±5,40 13,68±1,81 86,49±2,83 4,50±0,53 86,08±5,66 * IRS 4 96±3,44 2,90±0,61 94,33±3,78 1,90±0,32 92,67±3,78 1 89,33±10,98 LAB 16 81,38±11,80 8,78±0,94 76,92±12 4,00±1,63 66,86±11,34 5,40±2,07 78,81±13,44 LYM 18 77,01±8,08 9,05±1,03 77,97±6,92 6,50±0,71 70,67±12,61 6,40±1,65 78,70±14,22 MMG 21 65,80±6,31 9,18±1,49 64,03±6,33 0 - 0 - PRT 17 42,30±7,89 10,90±1,04 38,41±4,83 2,60±1,96 18,35±11,30 SON 60 82,65±7,49 28,33±1,79 88,58±2,69 0 - 1,70±0,48 54,52±8,26 SOYB 35 59,13±3,19 17,85±2 81,30±4,6 12,80±1,14 56,97±17,24 10,40±3,31 34,23±11,30 VEH 18 66,28±3,76 9,10±0,69 70,05±4,22 4 46,34±4,88 9,70±1,77 61,09±5,10 VOTE 17 100 8,90±1,13 100 8,40±3,06 100 1,70±0,82 100 VOW 13 97,47±2,39 9,58±0,61 96,52±1,62 1,00 16,67±1,86 WNE 13 74,76±11,18 5,95±0,55 94,53±4,56 1 62,90±8,91 10,20±0,42 88,11±11,14 ZOO 17 55,37±14,73 8,35±1,20 89,10±91,30 5,6±0,52 91,43±7,89 4,50±1,08 48,47±13,99 Promig 20,85 73,02 10,62 78,88 5,21 65,80 4,95 66,22 Motivació * Introducció als algorismes genètics Algorismes genètics en selecció d’atributs Disseny * Introducció a la selecció d’atributs Resultats Concl. i Línies de futur
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MARRIAGE REVOLUTIONS Companionate Revolution: Separating Home from Workplace  Fertility Revolution: Separating Sex from Procreation  Contraceptive Revolution: Separating Coitus from Contraception  Sexual Revolution: Separating Sex from Marriage  Reproductive Revolution: Separating Procreation from Marriage  Procreation Revolution: Separating Pregnancy from Parenthood 
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ONIOM Potential Energy Surface and Properties ONIOM energy E(ONIOM, Real) = E(Low,Real) + E(High,Model) - E(Low,Model) Potential energy surface well defined, and also derivatives are available. ONIOM gradient G(ONIOM, Real) = G(Low,Real) + G(High,Model) x J - E(Low,Model) x J J = (Real coord.)/ (Model coord.) is the Jacobian that converts the model system coordinate to the real system coordinate ONIOM Hessian H(ONIOM,Real) = H(Low,Real) + JT x H(High,Model) x J - JT x H(Low,Model) x J Scale each Hessian by s(Low)**2 or s(High)**2 to get scaled H(ONIOM) ONIOM density (ONIOM, Real) = (Low,Real) + (High,Model) - (Low,Model) ONIOM properties < o (ONIOM, Real)> = < o (Low,Real) > + < o (High,Model) > - < o (Low,Model) > from S. Irle
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SAP® In-Memory Computing Technology Executive Summary Enabling Real-Time Computing SAP® InMemory Computing technology enables realtime computing by bringing together online transaction processing (OLTP) applications and online analytical processing (OLA P) applications at a low total cost. Combining the advances in hardware technology with SAP InMemory Computing empowers the entire business – from shop floor to boardroom – by giving real-time business processes instantaneous access to data. The alliance of these two technologies can eliminate today’s information lag for your business. With the revolution of in-memory computing already under way, the question isn’t if this revolution will impact businesses but when and, more importantly, how. In-memory computing won’t be introduced because a company can afford the technology. It will be brought on board because a business cannot afford to allow its competitors to adopt the technology first. This paper details how in-memory computing can change the way you manage business intelligence and the value your business can derive from the technology. For business and IT executives, the paper furnishes substantial information and business examples about what changes they can look forward to and how those changes can catalyze their strategic initiatives. Where Does In-Memory Come into BI? Here is just a sampling of what in-memory computing technology can do for you: • Enable mixed workloads of analytics, operations, and performance management in a single software landscape. • Support smarter business decisions by providing increased visibility of very large volumes of business information • Enable users to react to business events more quickly through real-time analysis and reporting of operational data. • Provide greater flexibility by delivering innovative real-time analysis and reporting. • Support the deployment of innovative new business applications • Help streamline the IT landscape and reduce total cost of ownership (TCO). In manufacturing enterprises, in-memory computing technology will connect the shop floor to the boardroom, and the shop floor associate will have instant access to the same data as the board member [[shop floor = daily transaction processing. Boardroom = data mining]]. The shop floor will then see the results of their actions reflected immediately in the relevant Key Performance Indicators (KPI). SAP BusinessObjects Event Insight software is key. In what used to be called exception reporting, the software deals with huge amounts of realtime data to determine immediate and appropriate action for a real-time situation. Product managers will still look at inventory and point-of-sale data, but in the future they will also receive, for example, notifications when customers broadcast their dissatisfaction with a product to the masses over Twitter. Or they might be alerted to a negative product review released online that highlights some unpleasant product features requiring immediate action. From the other side, small businesses running real-time inventory reports will be able to announce to their Facebook and Twitter communities that a high demand product is available, how to order, and where to pick up. Bad movies have been able to enjoy a great opening weekend before crashing 2nd weekend when negative word-of-mouth feedback cools enthusiasm. That week-long grace period is about to disappear for silver screen flops. Consumer feedback won’t take a week, a day, or an hour. The very second showing of a movie could suffer from a noticeable falloff in attendance due to consumer criticism piped instantaneously through the new technologies. It will no longer be good enough to have the weekend numbers ready for executives on Monday morning. Executives will run their own reports on revenue, Twitter their reviews over the weekend, and by Monday morning have acted on their decisions.
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Experiment Results SUBJECTS 1 2 3 4 TEMPERATURE 55°F 70°F 55°F 70°F 55°F 70°F 55°F 70°F Heavy Metal 13 10 14 17 14 9 20 24 Classical 15 5 3 16 6 None 14 # # 23 2 21 1 8 11 21 10 24 4 Score (Response) Experiment Order 14 9 19 7 16 16 13 19 12 17 27 15 17 18 19 31 23 26 14 24 20 25 21 22
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Scientific Scientific Management Management Theory Theory • Evolution of Modern Management Began in the industrial revolution in the late 19th century as: • Managers of organizations began seeking ways to better satisfy customer needs. • Large-scale mechanized manufacturing began to supplanting small-scale craft production in the ways in which goods were produced. • Social problems developed in the large groups of workers employed under the factory system. • Managers began to focus on increasing the efficiency of the worker-task mix. © Copyright McGraw-Hill. All rights reserved. 2–4
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Scientific Scientific Management Management Theory Theory • Evolution of Modern Management Began in the industrial revolution in the late 19th century as: • Managers of organizations began seeking ways to better satisfy customer needs. • Large-scale mechanized manufacturing began to supplanting small-scale craft production in the ways in which goods were produced. • Social problems developed in the large groups of workers employed under the factory system. • Managers began to focus on increasing the efficiency of the worker-task mix. © Copyright McGraw-Hill. All rights reserved. 2–3
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ICP –Geography and Timeline ICP is a mandatory program currently operating in:  Greater Chicago Region – began 5-1-11, expanded to include City of Chicago 3-1-14 Rockford Region – began 7-1-13  Central Illinois Region – began 9-1-13  Metro East Region – began 9-1-13  Quad Cities Region – began 11-1-13 
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Care Coordination 2014 Roll Out Plan  February 2014 – Additional CCEs began serving SPD in ICP  March - June 2014 – MMAI (voluntary enrollment began in March, passive enrollment began in June)  March 2014 – ICP Expansion began in the city of Chicago  August 2014 – Family Health Program (FHP) began in the Metro East Region with plans to expand to all remaining mandatory regions  Fall2014 – MLTSS begins for those who opt-out of MMAI
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Georg Cantor (1845-1918) The Real Numbers are Uncountable Example: Show that the set of real numbers is uncountable. Solution: The method is called the Cantor diagnalization argument, and is a proof by contradiction. 1. Suppose R is countable. Then the real numbers between 0 and 1 are also countable (any subset of a countable set is countable - an exercise in the text). 2. The real numbers between 0 and 1 can be listed in order r1 , r2 , r3 ,… . 3. Let the decimal representation of this listing be 4. Form a new real number with the decimal expansion where 5. r is not equal to any of the r1 , r2 , r3 ,... Because it differs from ri in its ith position after the decimal point. Therefore there is a real number between 0 and 1 that is not on the list since every real number has a unique decimal expansion. Hence, all the real numbers between 0 and 1 cannot be listed, so the set of real numbers between 0 and 1 is uncountable. 6. Since a set with an uncountable subset is uncountable (an exercise), the set of real numbers is uncountable.
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Structs and Classes Structs A struct can be used to define a data structure type as follows: struct Complex { double real, imag;} // specifying a Complex type of objects void assign(Complex& w, double r, double i){w.real = r; w.imag=i;} Complex add(Complex a, Complex b){ Complex temp; temp.real = a.real + b.real; temp.imag = a.imag + b.imag; return temp;} main(){ Complex x,y,z; assign(x,1.15,2.0); assign(y,0.01,0.0); z = add(x,y);….} For data encapsulation, a struct can be used like a class, struct Complex{ private: double real,imag; public: assign(double r, double i){real = r; imag = i;} // similar to assign() above? Complex add(Complex a){ Complex temp; temp.real = real + a.real; temp.imag = imag + a.imag; return temp;} } main(){Complex x,y,z; x.assign(1.15,2.0); y.assign(0.01,0.0); z = x.add(y);….}
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the multBy method main(…) real Complex(double real, double imag) this 85638 real 3.0 r3 85638 Complex ref 85642 double imag double Complex i 85642 85642 Complex ref real double imag Complex ref other 85638 Complex ref 0.0 prod Complex ref 0.0 1.0 double Complex 85646 real 0.0 double imag 0.0 double Complex result 85646 Complex ref public Complex multBy(Complex other) { Complex result = new Complex(); // HERE result.real = this.real * other.real - this.imag * other.imag; result.imag = this.real * other.imag + this.imag * other.real; return result; } 8
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the multBy method main(…) real Complex(double real, double imag) this 85638 real 3.0 r3 85638 Complex ref 85642 double imag double Complex i 85642 85642 Complex ref real double imag Complex ref other 85638 Complex ref 0.0 prod Complex ref 0.0 1.0 double Complex 85646 real 0.0 double imag result 85646 Complex ref public Complex multBy(Complex other) { Complex result = new Complex(); result.real = this.real * other.real - this.imag * other.imag; result.imag = this.real * other.imag 3.0 double Complex + this.imag * other.real; // HERE return result; } 9
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From a Recent Talk (at 48 min in) by the founder of the Carbon Tax-and-Dividend Strategy – Climatologist Dr. James Hansen • “It’s time for the next Revolution – a revolution foreseen by our nation’s founders. A peaceful revolution. A revolution which must be led by young people, because they have the most at stake. The cancer in our political parties is too advanced.” • I completely agree. Not just Republicans, but Democrats as well - my generation has failed this planet. We knew the dangers of fossil fuels and did ~nothing about it. Even those with good hearts, continue to engage in the same old dysfunctional strategies which are proven by the evidence as failures. They cannot lead. It is the young people who must lead now. • As a member of the old generation, I can only offer my profound apologies for my generation’s failure, and to try to make as clear as I can to the new generations the science that must be the starting point for understanding where we are at and what we can do about it now.
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Evolution of Sustainable Competitive Advantage in Marketing  Pre-Industrial Age – interpersonal relationships were the greatest barrier to competitive attacks  Industrial Revolution – brands are important to signal product quality  Technology Revolution – offerings and innovations become key sources of differentiation  Services Revolution – all three BOR strategies are critical to success, but relationships are becoming more important with the shift to a service economy in more developed countries   Some researchers argue that developed counties are undergoing the next SCA revolution, due to the wider shift to a service economy Despite the shifting emphases on the different sources of SCA, all three sources (brands, offerings, and relationships) build on one another and often combine synergistically to determine a firm’s overall SCA © Palmatier 15
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• • • Russian Industrial Revolution was launched by the 1890s – focused on railroads and heavy industry – substantial foreign investment – industry was concentrated in a few major cities, large factories – growing middle class disliked Russia’s deep conservatism – Russian working class rapidly radicalized • harsh conditions • no legal outlet for grievances • large-scale strikes Insurrection breaks out in 1905, after Russia defeated by Japan – Moscow/St. Petersburg workers go on strike, create representative councils (“soviets”) – peasant uprisings, student demonstrations – non-Russian nationalities revolted – military mutiny – brutally suppressed, but forced the tsar’s regime to make reforms—fails to bring stability World War I provided the revolutionary moment – – Russian Revolution broke out in 1917, brought the most radical to power: Bolsheviks only in Russia did industrialization lead to violent social revolution • The Industrial Revolution and Latin America in the Nineteenth Century • • • • The four vice-royalties of Spanish America became eighteen separate countries international wars hindered development of the new nations Political life unstable, conservative forces backed by church were strong Military strongmen (caudillos) gained power, worked in conjunction with US/European corporations to develop extractive economies (agricultural—coffee, sugar, rubber, etc) using low paid labor
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Rotation and Revolution Like Earth’s moon (tidally locked to revolution around Earth), Mercury’s rotation has been altered by the sun’s tidal forces, but not completely tidally locked: Revolution period = 3/2 times rotation period Revolution: ≈ 88 days Rotation: ≈ 59 days  Extreme day-night temperature contrast: 100 K (-173 oC) – 600 K (330 oC)
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