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Plouffe went on to say that when we had problems it was because we did not step back to test our assumptions We had to ask, what s the strategy and do our assumptions align properly Decisions are based on strategy Optimizing enterprise performance requires knowing the strategy; oating the strategy to subordinate organizations such that they are contributing properly; and measuring contributions against the strategy How frequently do you change strategy Our answer is: as often as the data indicates that you should Bob Bragdon, Chief Security Of cer (CSOcom), commented: Assigning monetary value to data is a concept that few businesses seem to be embracing, although, even from a cursory examination, it would seem to make a great eal of sense to do We treat everything else in our organizations as assets with measurable nancial value: inventory, people, property, etc But when was the last time you sat down and calculated a dollar value for your customer database My guess is that it has never been done [7] Our view is that the situation is worse than Bragdon describes Executives do not associate assets with data and data with costs and value Accounting systems do not work that way, unless you move to activity-based cost accounting, for example In fact, the effort to introduce these practices needs resurgence and reinforcement Accounting rules and methods constrain the ability of companies to plan ahead and require addition and change to support capacity to forecast and predict In 2003, one of the authors (George) was at a conference at which Michael C Daconta talked about designing the smart data enterprise [8] Daconta was talking about how semantic computing would impact enterprise IT We really didn t remember this until Dr Brand Neiemann, at the CIO s of ce at the Environmental Protection Agency, brought it to our attention So, with this prompting we went back in time and found the reference If Daconta was talking on this subject in 2003, why is it not in wide practice today In fact, George was speaking about semantic interoperability at the same conference and is astonished at the lack of progress Daconta went on to become a metadata guru for the Department of Homeland Security, where we are certain he made signi cant contributions He laid the foundation in thought that should have advanced much further today Because IT messages do not often resonate with the executives and because executive leadership was in constant ux, progress is less than expected Daconta went on to write a book, The Semantic Web [9], published by Wiley, that surely made an impact Yet, how can we accelerate the progress that the concept deserves The answer is to engage executives in the subject in ways that matter to them most, as it should If we made a mistake, it is from not aligning the subject with the executive audience that most needs the solution A demonstrable example of how technology can take off when aligned with executives needs and interests is social media President Obama put the technology to work and now it has sprung an industry Of course, we may not imply that President Obama is responsible for social media, though we can say that he and David Plouffe demonstrated how to use it, as it put the community organizer on steroids, so to speak.

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the node ni V, Vcur to be scheduled next on processor procS (ni ) = P, it holds / that tdr,Sinsert,cur (ni , P) tdr,Send,cur (ni , P) tdr,S (ni , P), with the above argumentation With the end technique, ni is scheduled at the end of the last node already scheduled on P But this cannot be later than in S, because the nodes on P in Send,cur are the same nodes that are executed before ni on P in S, due to the schedule order of the nodes, and, according to the assumption, no node starts later than in S With the insertion technique, ni is scheduled in the worst case (ie, there is no idle period between two nodes already scheduled on P complying with Eq (61)) also at the end of the last node already scheduled on P Thus, ts,Sinsert,cur (ni , P) ts,Send,cur (ni , P) ts,S (ni , P) for node ni By induction this is true for all nodes of the schedule, in particular, the last node, which proves the theorem Theorem 51 of Section 51 establishes that an optimal schedule is de ned by the processor allocation and the nodes execution order List scheduling with the end technique can construct an optimal schedule from these inputs While this result is included in Theorem 61, it also establishes that the insertion technique might improve a given nonoptimal schedule Rescheduling a given schedule with list scheduling and the insertion technique, using the processor allocation and the node order of the original schedule, might improve the schedule length In particular, schedules produced with the end technique might be improved What rst sounds like a contradiction to Theorem 51, after all it states that the end technique is optimal given a processor allocation and the nodes execution order, becomes clear when one realizes that rescheduling with the insertion technique can only reduce the length of a schedule by reordering the nodes Inserting a node in an earlier slot changes the node order on the corresponding processor Complexity Regarding the complexity of list scheduling with the insertion technique, the second part of the corresponding Algorithm 9 is examined Determining the data ready time remains O(PE) for all nodes on all processors (see Section 51) What changes is the time complexity of the start time calculation In the worst case, it must be checked for every consecutive node pair on a processor, if the time period between them is large enough to accommodate the node to be scheduled At the time a node is scheduled, there are at most O(V) nodes scheduled on all processors So if the start time is determined for every processor as in the typical case of start time minimization this amortizes to O(max(V, P)), which is of course O(V), because it is meaningless to schedule on more processors than the task graph has nodes The start time is calculated for every node; thus, the nal complexity of the second part of simple list scheduling with start time minimization is O(V2 + PE) For comparison, with the end technique it is O(P(V + E)) (see Section 51) The insertion technique is employed n various scheduling heuristics, for example, ISH (insertion scheduling heuristic) by Kruatrachue [105], (E)CPFD ((economical) critical path fast duplication) by Ahmad and Kwok [4], BSA (bubble scheduling and allocation) by Kwok and Ahmad [114], and MD (mobility directed) by Wu and Gajski [207].

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