Controversy: Sweatshop Labor Defenders of sweatshops, such as Paul Krugman, claim that people choose to work in sweatshops because the sweatshops offer them substantially higher wages and better working conditions compared to their previous jobs of manual farm labor, and that sweatshops are an early step in the process of technological and economic development whereby a poor country turns itself into a rich country. Economists are focused on “trade offs” and when it comes to sweatshops, they ask whether the alternative of unemployment or even worse employment is better. In addition, sometimes when anti-sweatshop activists were successful in getting sweatshops to close, some of the employees who had been working in the sweatshops ended up starving to death, while others ended up turning to prostitution. BA 210 Lesson I.3 Trade 35
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SWEATSHOPS Merriam-Webster Dictionary defines sweatshop as a shop or factory in which workers are employed for long hours at low wages and under unhealthy conditions
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Assign Costs to Activity Cost Pools Overhead Costs at Classic Brass (Manufacturing and NonManufacturing) Activity Customer Orders Production Department Indirect factory wages Factory equipment depreciation Factory utilities Factory building lease General Administrative Department Administrative wages and salaries Office equipment depreciation Administrative building lease Marketing Department Marketing wages and salaries Selling expenses Total $ 125,000 Production Department $ CostIndirect Pools factory wages Factory equipment depreciation Product Order Customer Factory utilities Design Sizelease Relations Factory building Shipping costs traced to customer orders General Administrative Department Administrative wages and salaries Office equipment depreciation Administrative building lease Marketing Department Marketing wages and salaries Selling expenses Total overhead costs 500,000 300,000 120,000 Other 80,000 $ Total 1,000,000 40,000 400,000 50,000 60,000 510,000 250,000 50,000 $ 300,000 1,850,000 Indirect $500,000 Indirect factory factory wages wages $500,000 Percent 25% Percent consumed consumed by by customer customer orders orders 25% $125,000 $125,000
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Assign Costs to Activity Cost Pools Overhead Costs at Classic Brass (Manufacturing and NonManufacturing) Activity Customer Orders Production Department Indirect factory wages Factory equipment depreciation Factory utilities Factory building lease General Administrative Department Administrative wages and salaries Office equipment depreciation Administrative building lease Marketing Department Marketing wages and salaries Selling expenses Total $ 125,000 60,000 Production Department $ CostIndirect Pools factory wages Factory equipment depreciation Product Order Customer Factory utilities Design Sizelease Relations Factory building Shipping costs traced to customer orders General Administrative Department Administrative wages and salaries Office equipment depreciation Administrative building lease Marketing Department Marketing wages and salaries Selling expenses Total overhead costs 500,000 300,000 120,000 Other 80,000 $ Total 1,000,000 40,000 400,000 50,000 60,000 510,000 250,000 50,000 $ 300,000 1,850,000 Factory $300,000 Factory equipment equipment depreciation depreciation $300,000 Percent 20% Percent consumed consumed by by customer customer orders orders 20% $$ 60,000 60,000
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References References American Psychological Association. (2012). The basics of apa style. Retrieved from http://www.apastyle.org Angeli, E. (2012, May 9). General format. Retrieved from http://owl.english.purdue.edu/owl/resource/560/01/ Citation. (n.d.). Retrieved from Merriam-Webster online: http://www.merriam-webster.com/dictionary/citation Ivy Tech. (2008) Ivy tech citation handbook: Citing sources with mla & apa. Retrieved from http://wwwcc.ivytech.edu/shared/shared_librstatewidecc/pdf-files/citationhandbooks/flibrary-apa-mla-citationhandbook.pdf Plagiarize. (n.d.). Retrieved from Merriam-Webster online: http://www.merriam-webster.com/dictionary/plagiarize Russell, T. A. (2012, May 9). Mla formatting and style guide. Retrieved from the purdue owl. purdue u writing lab: http://owl.english.purdue.edu/owl/resource/747/01/ Wikipedia. (2012, May 16). The mla style manual. Retrieved from http://en.wikipedia.org/wiki/the_mla_style_manual
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#include using namespace std; #define WINDOWS class Widget { public: virtual void draw() = 0; }; class MotifButton : public Widget { public: void draw() { cout << "MotifButton\n"; } }; class MotifMenu : public Widget { public: void draw() { cout << "MotifMenu\n"; } }; class WindowsButton : public Widget { public: void draw() { cout << "WindowsButton\n"; } }; class WindowsMenu : public Widget { public: void draw() { cout << "WindowsMenu\n"; } }; class Factory { public: virtual Widget* create_button() = 0; virtual Widget* create_menu() = 0; }; class MotifFactory : public Factory { public: Widget* create_button() { return new MotifButton; } Widget* create_menu() { return new MotifMenu; } }; class WindowsFactory : public Factory { public: Widget* create_button() { return new WindowsButton; } Widget* create_menu() { return new WindowsMenu; } }; Factory* factory; void display_window_one() { Widget* w[] = { factory->create_button(), factory>create_menu() }; w[0]->draw(); w[1]->draw(); } void display_window_two() { Widget* w[] = { factory->create_menu(), factory>create_button() }; w[0]->draw(); w[1]->draw(); } void main() { #ifdef MOTIF factory = new MotifFactory; #else // WINDOWS factory = new WindowsFactory; #endif Widget* w = factory->create_button(); w->draw(); display_window_one(); Chapter 3 – Page 8 Multi-Platform w/Abstract Factory
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NEED-TO-KNOW 14-2 Following are the costs of a company that manufactures computer chips. Classify each as either a product cost or a period cost. Then classify each of the product costs as direct material, direct labor, or factory overhead. 1. Plastic board used to mount the chip 2. Advertising costs 3. Factory maintenance workers’ salaries 4. Real estate taxes paid on the sales office 1. Plastic board used to mount the chip 2. Advertising costs 3. Factory maintenance workers’ salaries 4. Real estate taxes paid on the sales office 5. Real estate taxes paid on the factory 6. Factory supervisor salary 7. Depreciation on factory equipment 8. Assembly worker hourly pay to make chips Product Costs All Factory Costs Assets on Balance Sheet 5. Real estate taxes paid on the factory 6. Factory supervisor salary 7. Depreciation on factory equipment 8. Assembly worker hourly pay to make chips Product Costs Direct Direct Factory Material Labor Overhead X Period Cost X X X X X X X Period Costs Non-Factory Costs Expensed on Income Statement as Selling, General and Administrative Learning Objective C2: Describe accounting concepts useful in classifying costs. Learning Objective C3: Define product and period costs and explain how they impact financial statements. 18 © McGraw-Hill Education
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Controversy: Sweatshop Labor A sweatshop is a working environment with unhealthy conditions that are considered by many people of industrialized nations to be difficult or dangerous, usually where the workers have few opportunities to address their situation. This can include exposure to harmful materials, hazardous situations, extreme temperatures, or abuse from employers. Sweatshop workers often work long hours for little pay, regardless of any laws mandating overtime pay or a minimum wage. Child labor laws may also be violated. BA 210 Lesson I.3 Trade 34
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DICTIONARIES Online Dictionary Heuristic. (n.d.). In Merriam-Webster’s online dictionary. Retrieved from http://www.m-w.com/dictionary/ Citation: (“Heuristic,” n.d.). Printed Dictionary Mish, F. (Ed.). (2007). Merriam-Webster’s collegiate dictionary (11th ed.) Springfield, MA: Merriam-Webster. Citation: (Mish, 2007). INSPIRATION for change
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NEED-TO-KNOW 16-4 Tower Mfg. estimates it will incur $200,000 of total overhead costs during the year. Tower allocates overhead based on machine hours; it estimates it will use a total of 10,000 machine hours during the year. During February, the assembly department of Tower Mfg. uses 375 machine hours. In addition, Tower incurred actual overhead costs as follows during February: indirect materials, $1,800; indirect labor, $5,700; depreciation on factory equipment, $8,000; factory utilities, $500. 2. Prepare journal entries to record (a) overhead applied for the assembly department for the month and (b) actual overhead costs used during the month. a) b) General Journal Work in Process Inventory Factory Overhead (375 machine hours x $20 per MH) Factory Overhead Raw Materials Inventory Factory Wages Payable Accumulated Depreciation - Factory Equipment Utilities Payable Debit 7,500 Credit 7,500 16,000 1,800 5,700 8,000 500 Factory Overhead Actual OH Incurred Ind. Materials Ind. Labor Fact. Deprec. Fact. Utilities OH Applied to Production 1,800 5,700 8,000 500 16,000 7,500 Underapplied 8,500 Learning Objective P3: Record the flow of factory overhead costs in process costing. © McGraw-Hill Education 32
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#include using namespace std; class Shape { public: Shape() { id = total++; } virtual void draw() = 0; protected: int id; static int total; }; class Factory { public: virtual Shape* createCurvedInstance() = 0; virtual Shape* createStraightInstance() = 0; }; class SimpleShapeFactory : public Factory { public: Shape* createCurvedInstance() { return new Circle; } Shape* createStraightInstance() { return new Square; } }; int Shape::total = 0; class Circle : public Shape { public: void draw() { cout << "circle " << id << ": draw" << endl; } }; class Square : public Shape { public: void draw() { cout << "square " << id << ": draw" << endl; } }; class RobustShapeFactory : public Factory { public: Shape* createCurvedInstance() { return new Ellipse; } Shape* createStraightInstance() { return new Rectangle; } }; void main() { #ifdef SIMPLE Factory* factory = new SimpleShapeFactory; #else Factory* factory = new RobustShapeFactory; #endif class Ellipse : public Shape { public: void draw() { cout << "ellipse " << id << ": draw" << endl; } }; class Rectangle : public Shape { public: void draw() { cout << "rectangle " << id << ": draw" << endl; } }; Shape* shapes[3]; shapes[0] = factory->createCurvedInstance(); shapes[1] = factory->createStraightInstance(); shapes[2] = factory->createCurvedInstance(); for (int i=0; i < 3; i++) shapes[i]->draw(); } Chapter 3 – Page 6 Shape Abstract Factory C++ Code
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Journal Entries for Multiple Departments     Work in Process – Blanking Work in Process – Forming Work in Process – Finishing Factory Overhead  Work in Process – Blanking Work in Process – Forming Work in Process – Finishing Factory Overhead       xx xx Work in Process – Forming Finished Goods  xx Work in Process – Blanking Work in Process – Finishing  xx Work in Process – Finishing xx xx xx Record Factory Overhead Work in Process – Blanking Work in Process – Forming Work in Process – Finishing     xx Factory Overhead Work in Process – Forming  xx xx xx xx Transfers to Various Departments  xx xx xx xx Payroll   xx xx Various Accounts Factory Overhead – Blanking Factory Overhead – Forming Factory Overhead – Finishing    Record Direct Labor       xx xx xx xx Materials Factory Overhead  Record Direct Materials  Factory Overhead - Blanking Factory Overhead – Forming Factory Overhead – Finishing xx xx xx xx xx xx   BLANKING  FORMING  FINISHING Department Department Department
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VOMmean on WINE150hl(FA,FS,TS,AL) 0 1 1 4 2 4 3 5 4 3 5 8 6 7 7 1 8 7 _________[0 9 2 . 10) 42 low 0 high CLUS_1.1.1.1.1.1 _________[0 . 19) 56 low 10 5 [10,16) 15 low 12 high CLUS 1.1.1.1.1.2 [19,22) 1 low 12 high 11 4 But no algorithm would pick 19 as a cut! ___12 _____[0 . 13) 56 low 5 1 low 12 high But no alg would pick a 13 cut! 13 7 [13,16) 14 3 _________[0 15 3 . 16) 57 low 12 high CLUS 1.1.1.1.1 35 high CLUS 1.1.1.1.2 17 2 [16,31) 0 low 18 5 19 4 20 3 21 1 22 4 23 4 24 5 25 1 26 2 27 1 29 2 30 1 31 1 _________[0 . 31) 57 low 47 high CLUS_1.1.1.1.1 32 1 28 high CLUS 1.1.1.1.2 34 1 [31,58) 0 low 36 1 37 3 38 1 39 2 40 1 43 6 44 4 46 2 47 1 48 1 50 1 52 1 _________[0 .58) 57 low 75 high CLUS_1.1.1.1 56 1 5 high CLUS 1.1.1.2 60 1 [58,70) 0 low 63 1 65 2 _________[0 .70) 57 low 80 high CLUS_1.1.1 67 1 [70,78) 0 low 2 high CLUS 1.1.2 72 1 _________[0 .78) 57 low 82 high CLUS_1.1 74 1 82 1 7 high CLUS1.2 83 1 [78,94) 0 low 85 1 86 1 87 2 _________[0.94) 57 low 89 high CLUS_1 88 1 99 1 0 low 4 high CLUS_2 105 1 113 1 119 1 d=VOMMEAN DPP on WINE_150_HL_(FA,FSO2,TSO2,ALCOHOL). Some agglomeration required: CLUS1.1.1.1.1.1 is LOW_Quality F[0,10], else HIGH Quality F[13,119] with 15 LOW error. Classification accuracy = 90% (if it had been cut 13, 99.3% accuracy!) 7 1 8 4 STDs=(1.9,9,23,1.2) 9 4 maxSTD=23 for 10 5 11 4 d=e TS on WN150hl(FA,FS,TS,AL 12 7 13 7 14 8 _________[0 CLUS 1.1.1.1.1.1 15 2 . 16) 42 low 12 high CLUS 1.1.1.1.1.2 16 5 [16,22) 15 low 17 4 18 5 . 19 7 20 3 _________[0 . 22) 57 low 12 high CLUS_1.1.1.1.1 21 3 32 high CLUS 1.1.1.1.2 23 2 [22,33) 0 low 24 9 25 3 26 1 27 4 28 4 29 5 30 1 31 2 _________[0 . 33) 57 low 44 high CLUS_1.1.1.1.1 32 1 31 high CLUS 1.1.1.1.2 34 2 [33,60) 0 low 35 1 36 1 37 1 39 1 41 1 42 3 43 1 44 2 45 1 47 6 48 4 49 2 50 1 51 1 53 1 55 1 _________[0 .60) 57 low 75 high CLUS_1.1.1.1 59 1 5 high CLUS 1.1.1.2 63 1 [60,72) 0 low 65 1 67 2 _________[0.72) 57 low 89 high CLUS1.1.1 69 1 74 1 [72,80] high CLUS1.1 CLUS1.1.2 _________[0.80) 57 low 892 high 75 1 84 1 7 high CLUS1.2 85 1 [80,95] 86 1 87 1 88 2 _________[0.95) 57 low 89 high CLUS1 89 1 100 1 4 high CLUS2 106 1 113 1 119 1 Identical cuts and accuracy! Tells us that d=eTotal_SO2 is responsible for all separation. WINE
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Field-At-A-Glance (Handout)    2015 – 2016 Generalist MSW Clinical MSW Learning Competencies 1. Assessing needs and resources 2. Providing resources 3. Obtaining resources (case  management) 4. Developing/Improving resources 1. Assessing needs and resources 2. Providing resources 3. Obtaining resources (case  management) 4. Developing/Improving resources 1. Clinical practice with individuals 2. Clinical practice with families 3. Clinical practice with groups Expectations   Entry-level practice Entry-level practice  Community-based clinical practice Type of Internship Spring Semester Only  Concurrent with course work Concurrent with course work Total Internship Hours 420 Total Field Hours 400 Total Field Hours 600 Total Field Hours Hours/Semesters in Field  (Full Time Students) 28 hours/week during Spring  semester  Average 14 hours/week Fall and  Spring semester (200 hours each  semester) Start 2nd week of Class. Average 20 hours/week Fall and  Spring semester (300 hours each  semester)   Hours/Semesters in Field  (Part Time Students) 17 hours/week Spring semester (250 hours)   14 hours/week Summer semester  (170 hours)  Average 10 hours/week Fall and  Spring (150 hours each semester)  Average 8 hours/week Summer  semester (100 hours)     Average 13 hours/week Fall and  Spring (200 hours each semester)  Average 16 hours/week Summer  semester (200 hours)  Supervision Required 1 hour/week with BSW or MSW 1 hour/week with MSW 1 hour/week with MSW FT – 1.5 hours every other week during  Fall and Spring semesters Field Seminar    FT – 3 hours/week during Spring  semester FT – 1.5 hours every other week during  Fall and Spring semesters PT – 2 hours/week Spring semester  and 1 hour/week Summer semester PT – 1 hour every other week during  Fall, Spring and Summer semesters PT – 1 hour every other week during  Fall, Spring and Summer semesters Field Seminar Instructor Field Seminar Instructor Field Seminar Instructor Field Paperwork turned in to: January 2016 BSW   University of Central Florida - Office of Field Education 5
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Field-At-A-Glance (Handout)    2015 – 2016 Generalist MSW Clinical MSW Learning Competencies 1. Assessing needs and resources 2. Providing resources 3. Obtaining resources (case  management) 4. Developing/Improving resources 1. Assessing needs and resources 2. Providing resources 3. Obtaining resources (case  management) 4. Developing/Improving resources 1. Clinical practice with individuals 2. Clinical practice with families 3. Clinical practice with groups Expectations   Entry-level practice Entry-level practice  Community-based clinical practice Type of Internship Spring Semester Only  Concurrent with course work Concurrent with course work Total Internship Hours 420 Total Field Hours 400 Total Field Hours 600 Total Field Hours Hours/Semesters in Field  (Full Time Students) 28 hours/week during Spring  semester  Average 14 hours/week Fall and  Spring semester (200 hours each  semester) Start 2nd week of Class. Average 20 hours/week Fall and  Spring semester (300 hours each  semester)   Hours/Semesters in Field  (Part Time Students) 17 hours/week Spring semester (250 hours)   14 hours/week Summer semester  (170 hours)  Average 10 hours/week Fall and  Spring (150 hours each semester)  Average 8 hours/week Summer  semester (100 hours)     Average 13 hours/week Fall and  Spring (200 hours each semester)  Average 16 hours/week Summer  semester (200 hours)  Supervision Required 1 hour/week with BSW or MSW 1 hour/week with MSW 1 hour/week with MSW FT – 1.5 hours every other week during  Fall and Spring semesters Field Seminar    FT – 3 hours/week during Spring  semester FT – 1.5 hours every other week during  Fall and Spring semesters PT – 2 hours/week Spring semester  and 1 hour/week Summer semester PT – 1 hour every other week during  Fall, Spring and Summer semesters PT – 1 hour every other week during  Fall, Spring and Summer semesters Field Seminar Instructor Field Seminar Instructor Field Seminar Instructor Field Paperwork turned in to: August 2015 - 16 BSW   University of Central Florida - Office of Field Education 31
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//******************************************************************** // Words.java Author: Lewis/Loftus // // Demonstrates the use of an inherited method. //******************************************************************** public class Words { //----------------------------------------------------------------// Instantiates a derived class and invokes its inherited and local methods. //----------------------------------------------------------------public static void main (String[ ] args) { Dictionary webster = new Dictionary(); System.out.println ("Number of pages: " + webster.getPages()); System.out.println ("Number of definitions: " + webster.getDefinitions()); System.out.println ("Definitions per page: " + webster.computeRatio()); } // end main() } // end Words Looks simple enough. Creating an object webster of type Dictionary. Then we are executing a few of webster’s methods (from the class…) So, we need to see what the Dictionary class looks like, right? Nothing new here at this time… © 2004 Pearson Addison-Wesley. All rights reserved 8-8
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//******************************************************************** // Words.java Author: Lewis/Loftus // // Demonstrates the use of an inherited method. //******************************************************************** public class Words { //----------------------------------------------------------------// Instantiates a derived class and invokes its inherited and local methods. Where from? //----------------------------------------------------------------NOT in public static void main (String[] args) Dictionary!! { Dictionary webster = new Dictionary(); System.out.println ("Number of pages: " + webster.getPages()); System.out.println ("Number of definitions: " + webster.getDefinitions()); System.out.println ("Definitions per page: " + webster.computeRatio()); } // end main() } // end Words We KNOW where these are from. getDefinitions() and computeRatio() are in Dictionary getPages() is in the base class, Book! © 2004 Pearson Addison-Wesley. All rights reserved 8-11
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Discussion  Are Sweatshops Ethically acceptable?  As a top manager of a business, would you choose to move your factory to an underdeveloped country for better profit? Why or why not?  Do third world standards justify sweatshops?  What steps could managers of sweatshops take to improve the conditions?
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NeMoFinder adapts SPIN [27] to extract frequent trees and expands them into non-isomorphic graphs.[8] NeMoFinder utilizes frequent size-n trees to partition the input network into a collection of size-n graphs, afterward finding frequent size-n sub-graphs by expansion of frequent trees edge-by-edge until getting a complete size-n graph Kn. The algorithm finds NMs in undirected networks and is not limited to extracting only induced sub-graphs. Furthermore, NeMoFinder is an exact enumeration algorithm and is not based on a sampling method. As Chen et al. claim, NeMoFinder is applicable for detecting relatively large NMs, for instance, finding NMs up to size-12 from the whole S. cerevisiae (yeast) PPI network as the authors claimed.[28] NeMoFinder consists of three main steps. First, finding frequent size-n trees, then utilizing repeated size-n trees to divide the entire network into a collection of size-n graphs, finally, performing sub-graph join operations to find frequent size-n sub-graphs.[26] In the first step, the algorithm detects all non-isomorphic size-n trees and mappings from a tree to the network. In the second step, the ranges of these mappings are employed to partition the network into size-n graphs. Up to this step, there is no distinction between NeMoFinder and an exact enumeration method. However, a large portion of non-isomorphic size-n graphs still remain. NeMoFinder exploits a heuristic to enumerate non-tree size-n graphs by the obtained information from preceding steps. The main advantage is in third step, which generates candidate sub-graphs from previously enumerated sub-graphs. This generation of new size-n sub-graphs is done by joining each previous sub-graph with derivative sub-graphs from itself called cousin sub-graphs. These new sub-graphs contain one additional edge in comparison to the previous sub-graphs. However, there exist some problems in generating new sub-graphs: There is no clear method to derive cousins from a graph, joining a sub-graph with its cousins leads to redundancy in generating particular sub-graph more than once, and cousin determination is done by a canonical representation of the adjacency matrix which is not closed under join operation. NeMoFinder is an efficient network motif finding algorithm for motifs up to size 12 only for protein-protein interaction networks, which are presented as undirected graphs. And it is not able to work on directed networks which are so important in the field of complex and biological networks. The pseudocode of NeMoFinder is shown here: NeMoFinder Input: G - PPI network; N - Number of randomized networks; K - Maximal network motif size; F - Frequency threshold; S - Uniqueness threshold; Output: U - Repeated and unique network motif set; D ← ∅; for motif-size k from 3 to K do T ← FindRepeatedTrees(k); GDk ← GraphPartition(G, T) D ← D ∪ T; D′ ← T; i ← k; while D″ = ∅ and i ≤ k × (k - 1) / 2 do D′ ← FindRepeatedGraphs(k, i, D′); D ← D ∪ D′; i ← i + 1; end while end for for counter i from 1 to N do Grand ← RandomizedNetworkGeneration(); for each g ∈ D do GetRandFrequency(g, Grand); end for end for U ← ∅; for each g ∈ D do s ← GetUniqunessValue(g); if s ≥ S then U ← U ∪ {g}; end if end for return U Grochow and Kellis [29] proposed an exact alg for enumerating sub-graph appearances, which is based on a motif-centric approach, which means that the frequency of a given sub-graph,called the query graph, is exhaustively determined by searching for all possible mappings from the query graph into the larger network. It is claimed [29] that a motif-centric method in comparison to network-centric methods has some beneficial features. First of all it avoids the increased complexity of sub-graph enumeration. Also, by using mapping instead of enumerating, it enables an improvement in the isomorphism test. To improve the performance of the alg, since it is an inefficient exact enumeration alg, the authors introduced a fast method which is called symmetry-breaking conditions. During straightforward sub-graph isomorphism tests, a sub-graph may be mapped to the same sub-graph of the query graph multiple times. In Grochow-Kellis alg symmetrybreaking is used to avoid such multiple mappings. GK alg and symmetry-breaking condition which eliminates redundant isomorphism tests. (a) graph G, (b) illustration of all automorphisms of G that is showed in (a). From set AutG we can obtain a set of symmetrybreaking conditions of G given by SymG in (c). Only the first mapping in AutG satisfies the SynG conditions; so, by applying SymG in Isomorphism Extension module alg only enumerate each match-able sub-graph to G once. Note that SynG is not a unique set for an arbitrary graph G. The GK alg discovers the whole set of mappings of a given query graph to the network in two major steps. It starts with the computation of symmetry-breaking conditions of the query graph. Next, by means of a branch-and-bound method, alg tries to find every possible mapping from the query graph to the network that meets the associated symmetry-breaking conditions. Computing symmetry-breaking conditions requires finding all automorphisms of a given query graph. Even though, there is no efficient (or polynomial time) algorithm for the graph automorphism problem, this problem can be tackled efficiently in practice by McKay’s tools.[24][25] As it is claimed, using symmetry-breaking conditions in NM detection lead to save a great deal of running time. Moreover, it can be inferred from the results in [29][30] that using (a) graph G, (b) illustration of all automorphisms of G that is showed in (a). From set AutG we can obtain a set the symmetry-breaking conditions results in high efficiency particularly for directed networks in comparison to undirected of symmetry-breaking conditions of G given by SymG networks. The symmetry-breaking conditions used in the GK algorithm are similar to the restriction which ESU algorithm in (c). Only the first mapping in AutG satisfies the applies to the labels in EXT and SUB sets. In conclusion, the GK algorithm computes the exact number of appearance of a SynG conditions; as a result, by applying SymG in the given query graph in a large complex network and exploiting symmetry-breaking conditions improves the algorithm Isomorphism Extension module the algorithm only performance. Also, GK alg is 1 of the known algorithms having no limitation for motif size in implementation and potentially enumerate each match-able sub-graph in network to G once. SynG is not a unique set for an arbitrary graph G. it can find motifs of any size.
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Compute Unit Profit factory 1 factory 1 factory 1 factory 1 factory 2 factory 2 factory 2 factory 2 line 1 line 1 line 2 line 2 line 1 line 1 line 2 line 2 product A product B product A product B product A product B product A product B XA11 XB11 XA12 XB12 XA21 XB21 XA22 XB22 selling price line cost/hr line hr/unit line unit cost raw mat unit cost raw mat unit/prod raw mat cost/unit labor hr/unit labor rate $/hr labor unit cost factory burden rate burden unit cost total unit cost unit profit fixed cost 4600 132 8 1056 102 10 1020 30 48 1440 0.7 1008 4524 76 1030 6000 132 12 1584 62.5 13 812.5 43 48 2064 0.7 1444.8 5905.3 94.7 950 4600 130 8 1040 102 10 1020 30 48 1440 0.7 1008 4508 92 1200 6000 130 12 1560 62.5 13 812.5 43 48 2064 0.7 1444.8 5881.3 118.7 1200 4600 136 8 1088 102 10 1020 30 45 1350 0.8 1080 4538 62 900 6000 136 12 1632 62.5 13 812.5 43 45 1935 0.8 1548 5927.5 72.5 1200 4600 138 8 1104 102 10 1020 30 45 1350 0.8 1080 4554 46 1400 6000 138 12 1656 62.5 13 812.5 43 45 1935 0.8 1548 5951.5 48.5 1500
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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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