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A speci c modeling technique has evolved in order to upport the types of queries and analyses that businesses require This technique is called dimensional modeling This approach has been applied to data warehousing for nearly thirty years and is supported by a wide variety of database platforms and data access or business intelligence tools Dimensional models support the business perspective of the data, and today s technology ensures that they can be effectively implemented Dimensional modeling is a formal data modeling technique that is used to organize and present data for analytical and reporting use The focus is on the business perspective and representation of data The goal is to free the data that has been captured and stored by the operational systems and make it available to the business community Regardless of how data is structured, business people will ask questions based upon their frame of reference This perspective is driven by the basic characteristics of the industry and how the company is organized, so why not organize the data to re ect this business perspective The two primary goals for dimensional modeling are ease of use and query performance These are the principles that guide the entire dimensional modeling process There are other data modeling techniques that play an important part in overall systems development They help ensure that the data itself and the relationships between different data elements are clearly de ned For operational systems, it is important that the data be organized to facilitate transaction processing This includes ensuring transaction integrity and speed The type of modeling used for operational system design is called entity-relationship (E-R) modeling This may also be referred to as normalized modeling One speci c form of E-R modeling represents the data in third normal form (3NF) There is a complete discipline surrounding this approach to data modeling This is mentioned to acknowledge the value and purpose of E-R modeling for operational system design The following two sections examine the main objectives of dimensional modeling.



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Complexity In comparison to contention aware list scheduling, the time complexity under the involvement contention model does not increase The additional scheduling of the edge on the processors increases the effort to implement the algorithm, but it does not modify its time complexity For example, the complexity of the second part of simple list scheduling (Algorithm 9) with start/ nish time minimization and the end technique is O(P(V + EO(routing))) (see Section 751) Also, the complexity does not increase for the insertion technique (Section 61), even though the individual scheduling of a node and an edge on a processor is more complex In the involvement contention model, there are more objects on the processors, namely, nodes and edges, which must be searched to nd an idle time interval On all processors there are at most O(V + E) nodes and edges; hence, the total complexity for the scheduling of the nodes increases from O(V2 ), under the contention model, to O(V(V + E)) The scheduling of the edges on the processors is O(E(V + E)), compared to O(PE) with the end technique, and on the links it remains O(PE2 O(routing)) Hence, the total complexity of the second part of simple list scheduling with start/ nish time minimization and the insertion technique is O 2 + VE + PE2 O(routing)), that is, O(V2 + PE2 O(routing)), as under the contention model (Section 751) 842 Two-Phase Heuristics The generic form of a two-phase scheduling algorithm was already outlined in Algorithm 13 The rst phase establishes the processor allocation and the second phase constructs the actual schedule with a simple list scheduling heuristic, as described in Section 52 The next paragraphs discuss how such an algorithm is applied under the involvement contention model Phase 1 Processor Allocation The processor allocation can originate from any heuristic or can be extracted from a given schedule For example, a schedule produced under the classic or contention model might serve as the input (see also Section 52) Using the rst two steps of a clustering based heuristic, that is, the clustering itself and the cluster-to-processor mapping (Algorithm 14, Section 53), is very promising, because clustering tries to minimize communication costs, which is even more important under the involvement contention model The clustering itself can be performed under the classic or, as described in Section 753, under the contention model Even the involvement contention model can be used with a straightforward extension of the considerations made in Section 753 Genetic Algorithm In Sinnen [172] and Sinnen et al [180], a genetic algorithm, called GICS (genetic involvement contention scheduling), is proposed for the determination of the processor allocation The simple idea is that the genetic algorithm searches for an ef cient processor allocation, while the actual scheduling is performed with a list scheduling heuristic as discussed later for phase 2.

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