Accurate estimation of cache-related preemption delay Multitasked real-time systems often employ caches to boost performance. However the unpredictable dynamic behavior of caches makes schedulability analysis of such systems difficult. In particular, the effect of caches needs to be considered for estimating the inter-task interference. As the memory blocks of different tasks can map to the same cache blocks, preemption of a task may introduce additional cache misses. The time penalty introduced by these misses is called the Cache-Related Preemption Delay (CRPD).In this paper, we provide a program path analysis technique to estimate CRPD. Our technique performs path analysis of both the preempted and the preempting tasks. Furthermore, we improve the accuracy of the analysis by estimating the possible states of the entire cache at each possible preemption point rather than estimating the states of each cache block independently. To avoid incurring high space requirements, the cache states can be maintained symbolically as a Binary Decision Diagram. Experimental results indicate that we obtain tight CRPD estimates for realistic benchmarks. Real-time embedded systems involve tasks operating under deadlines which need to be met. Consequently schedu-Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. CODES+ISSS'03, October 1-3, 2003 , Newport Beach, California, USA. Copyright 2003 ACM 1-58113-742-7/03/0010 ..$5.00 lability analysis of such tasks has been a topic of active research. For each task, schedulability analysis needs to know the worst case execution time (WCET), that is, an upper bound on the task execution time. For preemptive task scheduling, we also need to estimate the inter-task interference (the additional execution time introduced when a high priority task preempts a low priority task). However, performance enhancing micro-architectural features of modern processors make such estimation difficult.. Ideally, the WCET of a program should be the cost of its longest path. However, this assumes that the execution time of each instruction is constant. In reality, the execution time of a memory access instruction depends on the past instructions executed (which decides whether it is a cache hit/miss). Thus, path analysis of the program cannot be done at the level of assembly code; it also needs to take into account the effect of micro-architectural features such as caches [5, 9, 11, 14] . This explains the importance of cache behavior modeling for WCET analysis.. Caches have a role to play in inter-task interference estimation as well. Consider a low priority task τ which is preempted by a higher priority task τ . Let the set of cache blocks to which the memory blocks of τ (τ ) get mapped be Cτ (C τ ). If Cτ ∩ C τ = ∅ then τ may displace some memory blocks of τ from the cache, resulting in additional cache misses when τ resumes. This effect of task preemption on cache performance is known in the literature as Cache-Related Preemption Delay (CRPD).. CRPD estimation is important from the point of view of schedulability analysis. In particular, [7, 8] reports how CRPD can be used to derive accurate response times of multiple periodic tasks running on a single processor for fixedpriority preemptive scheduling. We can avoid CRPD estimation by ignoring arbitrary preemption (as in [10] which only studies the interference at the beginning of a periodic task's execution). Alternatively, we can employ cache partitioning [6] , where each task is allowed to use only a portion of the cache. However, this leads to severe degradation of performance. Thus, for multitasked preemptive real-time systems with caches, CRPD estimation is necessary.. When a task τ is preempted by a higher priority task τ what are the additional cache misses introduced? To answer this question, we need to consider the following factors. First, all the memory blocks of τ which are in the cache when τ is preempted may not be re-referenced after resumption. Secondly, all the memory blocks of τ which are in the cache when τ is preempted may not be replaced by τ . Thirdly, there are several possible cache contents when τ is preempted (resulting from the different paths of τ ), and when τ resumes execution (resulting from the different paths of τ .) Last but not the least, there are several possible memory reference patterns after τ resumes execution (due to different paths of τ ).. The importance of some of these factors on CRPD is mentioned in [4] . However, no estimation technique is given and only simulation results are presented. The work of Lee et. al. [7] performs set based analysis of the cache blocks used by the preempted task τ before and after preemption. This is extended in [8] to also consider the set of cache blocks used by the high-priority task τ . Tomiyama and Dutt perform implicit path analysis of the preempting task τ but ignore the effects of the preempted task τ [13] .. In this paper, we propose methods to improve the accuracy of CRPD estimation. As per existing works [7, 8, 13] , we only consider the effects of the instruction cache (and not data cache). Our technique performs path analysis of both τ and τ . Furthermore, we compute the possible states of the cache when the lower priority task τ is preempted and when the higher priority task τ is completed. This is more accurate than existing set-based analysis techniques which estimate the cache states by inferring the set of memory blocks which may exist in each cache block. Suppose that the cache has two blocks and the possible states of the cache when τ is preempted are as follows { m0, m1 , m2, m3 } In the above m0, m1, m2, m3 are memory blocks with m0, m2 mapping to the first cache block and m1, m3 mapping to the second cache block. Existing path analysis techniques such as [7, 8] will report the possible states as follows. In this paper, we have studied the problem of estimating Cache-Related Preemption Delay (CRPD) in preemptive multitasked real-time systems. CRPD is the delay introduced in a low-priority task owing to additional cache misses caused via preemption (by a higher priority task). To enable accurate analysis, our approach maintains possible cache contents (1) at each preemption point of the low-priority task and (2) at the end of high-priority task. Our experimental results show that our approach leads to tighter estimates than existing approaches which estimate the possible contents of each cache block separately (for the low-priority as well as the high-priority task). Note that in our analysis, we have throughout assumed only two tasks. Extending the technique to m tasks is straightforward: we need to consider all possible preemption sequences.. One possible concern regarding our analysis technique is a blow-up in space consumption. As observed in the previous section, none of our benchmarks suffered from an exponential space blow-up due to our decision to represent cache states (instead of the content of each cache block separately). Furthermore, we can handle possible blow-up in the representation of reaching/live cache states by adopting a symbolic representation of LCSB and RCSB. In particular, we can represent LCSB and RCSB (for any basic block B) as a Binary Decision Diagram (BDD). A BDD is a compact representation for a boolean function [1] . Consider a direct mapped cache with n cache blocks and at most k memory blocks mapping to each cache block. The content of any cache block in a cache state can then be captured with log2(k + 1) boolean variables. For example if two memory blocks m and m can map to a cache block, then the cache block can have three possible contents: m, m and ⊥ (that is, it does not hold any memory block). These three possibilities can be captured with log2(3) = 2 boolean variables. A cache state simply denotes the content of all the n cache blocks. Therefore, a cache state can be represented via truth assignment of n * log2(k + 1) boolean variables. A set of cache states S simply denotes a set of truth assignments of these n * log2(k + 1) boolean variables, call it VS. Thus, a set of cache states S can be represented as a propositional logic formula (or a BDD) fS over the n * log2(k+1) boolean variables (s.t. fS is true for only the truth assignments in VS). We use this BDD representation for LCSB and RCSB (for any basic block B) since they are simply sets of cache states. The variable ordering in the BDD representation is obtained from a total ordering of the cache blocks. Given the BDD representation for LCSB and RCSB, the fixed point equations for computing these quantities (given in Section 2.1) are modified in a standard fashion. We have used the Colorado University Decision Diagrams (CUDD) package [3] for computing the quantities LCSB, RCSB as BDDs. However, none of our benchmarks suffered from an exponential space blow-up in the number of cache states appearing in LCS or RCS. Therefore, the space gains obtained from the symbolic BDD representation (as compared to our enumerative set based representation) were modest in all our benchmarks. In future, we will conduct further experiments to evaluate the need for symbolic representation of cache states in our analysis technique. We select nine different benchmarks for our experiments (refer Table 2 ). Most of these benchmarks are from [12] and [11] . We use the Simplescalar architectural simulation platform [2] in the experiments. All the benchmarks are compiled to Simplescalar assembly language with modified gcc. A prototype analyzer written by us accepts assembly language code, disassembles it, identifies the basic blocks and constructs the control flow graph (CFG). Given the CFG of the low-priority and the high-priority task as well as the instruction cache configuration, our analyzer computes LP Task HP Task matsum eqntott sqrt A C S A C S A C S qsort 19 20 24 16 22 28 18 19 26 crc 17 17 18 17 21 22 18 19 20 ssearch 19 22 23 19 25 27 21 22 25 des 22 23 24 21 24 26 22 22 25 whet 20 21 22 20 25 25 23 23 24 math 18 22 23 20 25 27 20 22 25 Table 3 : Accuracy of CRPD analysis for a 32-block cache. A stands for actual number of misses in CRPD (found by exhaustive simulation), C stands for our combined analysis of all cache blocks and S stands for separate analysis of each cache block. Table 4 : Maximum number of cache blocks used by high priority task for a 32-block cache.. the CRPD of the tasks. Our analyzer is parameterizable with respect to cache block size and number of cache blocks.

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How feedback boosts motivation and play in a brain-training game Games are important vehicles for learning and behavior change as long as players are motivated to continue playing. We study the impact of verbal feedback in stimulating player motivation and future play in a brain-training game. We conducted a 2 (feedback valence: positive vs. negative) Â 3 (feedback type: descriptive, comparative, evaluative) between-subjects experiment (N = 157, 69.4% female, M age = 32.07). After playing a brain-training game and receiving feedback, we tapped players' need satisfaction, motivation and intention to play the game again. Results demonstrate that evaluative feedback increases, while comparative feedback decreases future game play. Furthermore, negative feedback decreases players' feeling of competence, but also increases immediate game play. Positive feedback, in contrast, satisfies competence and autonomy needs, thereby boosting intrinsic motivation. Negative feedback thus motivates players to repair poor short-term performances, while positive feedback is more powerful in fostering long-term motivation and play. Games are an increasingly important mechanism for educational and behavior-change interventions due to their ability to keep players motivated to play (Baranowski, Buday, Thompson, & Baranowski, 2008; Erhel & Jamet, 2013) . However, the mechanisms by which games motivate players to persist in game play are still unclear. While several models of game motivation have been developed, most are typologies of uses and gratifications derived from games (Lucas & Sherry, 2004) common player types (Yee, 2006) or based in usability studies from HCI or persuasive technology (Fogg, 2007) . Recently, psychological theories which can explicate motivational processes in other areas of life have been applied successfully to understand how and why people continue playing entertainment games. Namely, Self Determination Theory (SDT) has been used to explicate game enjoyment, desire to play, and role of games in changing player behavior beyond the game world (cf. Przybylski, Rigby, & Ryan, 2010; Deterding, Sicart, Nacke, O'Hara, & Dixon, 2011) . According to SDT, the intrinsic appeal of games is due to their ability to satisfy basic psychological needs for competence, autonomy, and relatedness (Ryan, Rigby, & Przybylski, 2006 ). Yet, two challenges still remain in terms of using SDT to understand the motivation to play educational or behavior-change type games. First, although intrinsic motivation is central to games for entertainment, the effects of intrinsic motivation on continued play in education and behavior change games is still not well understood. Second, the particular game elements of educational games which satisfy these basic psychological needs have only begun to be explored (Peng, Lin, Pfeiffer, & Winn, 2012) . One element in particular which is critical to education and behavior-change games is feedback, such as verbal or non-verbal messages delivered in-game (Lester, Stone, Converse, Kahler, & Barlow, 1997) . The current study, therefore, tests the role of feedback in an educational brain-training game on intrinsic motivation to continue playing, enjoyment of the game, and attitude towards the agent.. Feedback can have differential effects in terms of motivating behaviors, and results testing the effects of feedback in games on motivation have been inconclusive (Lin, Atkinson, Christopherson, Joseph, & Harrison, 2013) . After all, feedback can have disparate effects on motivation based on how it is delivered, how the recipient interprets the feedback, and how the behavior is related to the feedback. In the health-behavior domain, Feedback Intervention Theory (FIT, Kluger & DeNisi, 1996) has been used to account for these differential effects. Feedback can compare, evaluate, or simply describe performance. These three types of feedback have very distinct effects on performance and motivation for health behaviors (cf. Hawkins, Kreuter, Resnicow, Fishbein, & Dijkstra, 2008) . To the best of our knowledge, these questions have not yet been explored in the context of educational games.. The current study thus extends the literature on educational and behavior-change games in three ways: by (1) using FIT to understand specific effects of different types of feedback on persistence and motivation within a game environment, (2) by examining the role of FIT in terms of satisfying basic psychological needs in games and (3) by further unraveling the relationship between basic game and motivational processes. Our study shows the potential of extending the motivational framework of games as understood via CET by including the relevance of feedback (FIT; Kluger & DeNisi, 1996) . We found that both negative and positive feedback can positively enhance willingness to engage in continued play of serious games. Players receiving negative feedback feel less competent and want to immediately repair their performance. This indicates that negative feedback may, counter-intuitively, be good in stimulating short-term and immediate gaming behavior. Gamers receiving positive feedback, on the other hand, felt more competent and autonomous and desirous of playing the future. This suggests that positive feedback may be good in sustaining long-term play. Studying the impact of theoretically relevant game elements such as feedback thus demonstrates the potential strengths and weakness of including these types of elements in serious games and gamified applications. Future research will do well to examine these elements and their subsequent effects in large field studies of player behavior outside the lab, in order to best understand how to motivate players of behavior-change games. The aim of this study was to understand the role of feedback in intrinsic motivation to play an online brain-training game. We combined FIT with CET to understand specific effects of different types of feedback on need satisfaction in game, subsequent effects on motivation, and free-choice behavior. Our findings overall replicate past research on the role of intrinsic motivation and desire to play games. We found that intrinsic motivation is positively related to choice to play the game again now and in the future (see also Przybylski et al., 2010; Ryan et al., 2006; Tamborini et al., 2010) . Supporting the notion that intrinsic motivation is most critical to gaming behavior, even with regards to non-entertainment games, we found no significant effect of extrinsic motivations on desire to play the game immediately or in the future. These findings provide additional empirical support for the hypothesis of CET that most gamers are intrinsically motivated, and that understanding the determinants of intrinsic motivation is critical to understanding how games may best motivate players to continue playing.. Our first hypothesis predicted that intrinsic motivation would be a result of perceived need satisfaction in game. In line with H1, we indeed found the expected relationships, in that competence and autonomy need satisfaction was positively related to intrinsic motivation. In terms of need satisfaction and free choice behavior, we found that as expected a high perception of autonomy-need satisfaction, as well as high intrinsic motivation, increased the likelihood to play the game again immediately and in the future. These findings underscore the importance of satisfying intrinsic needs for autonomy in motivating people to play educational games.. However, H1 was disconfirmed for competence-need satisfaction: players who felt low in competence after play (i.e. less competence need satisfaction) were likelier to immediately play again compared to those experiencing high competence satisfaction. This effect can perhaps best be explained by the type of game we used. The success of a ''brain training'' game in which a particular skill is tested or trained may actually depend on people feeling less competent, so that they desire to improve in the future. Thus, if players feel very competent with regards to the skill tested in the game, they have no reason to continue playing in the future.. Our second hypothesis predicted that participants receiving positive feedback would feel themselves to be more competent and autonomous, which in turn, would increase intrinsic motivation. This hypothesis was confirmed. This demonstrates that feedback can be an important motivator and thus an important addition to the literature on motivation (e.g., . We found that feedback valence affected perceived competence and autonomy, in that participants receiving positive feedback (vs. negative feedback) perceived themselves as more competent and autonomous. The mediation analysis additionally revealed positive indirect effects of feedback valence on intrinsic motivation through both perceived competence and autonomy. Although the direct effect of positive feedback on intrinsic motivation is negative, once adding the mediating variables of competence and autonomy, the overall relationship is positive. This may be explained by the perception of positive feedback as less controlling than negative feedback (Deci et al., 1999) . This implies that it is useful to further explore how feedback interventions (as predicted by FIT, Kluger & DeNisi, 1996) can satisfy competence and autonomy needs to increase intrinsic motivation in a range of behavioral applications.. Additionally, we found a surprising result of feedback valence on game play based on the time frame that play would take place: results show that participants receiving negative feedback were likelier to play the game immediately than participants receiving positive feedback. This finding is conceptually similar to our findings regarding competence need satisfaction. Perhaps players wanted to play again immediately in order to redeem themselves, or to succeed better at the task. This implies that a reduction of competence can lead to more game play in brain-training games, because players want to immediately repair and improve their performance. These findings are in line with work by Reinecke et al. (2012) who found that thwarted competence needs in a falsefeedback task led to desire to restore competence needs via immediately playing a video game at a lower difficulty level. In essence, our negative feedback may have acted like an initial task in a false-feedback manipulation, such that we would see the expected positive relationships between competence and motivation in the second round of play. It could also be that prior studies (Reinecke et al., 2012; Tamborini et al., 2010 Tamborini et al., , 2011 were more interested in enjoyment of the game vs. desire to play again -it could well be that competence has differential effects for enjoyment of current play vs. desire to play again.. Our hypotheses regarding feedback effectiveness specifically predicted that evaluative feedback would be most effective (H3a), followed by comparative feedback (H3b). Receiving evaluative feedback indeed increased the likelihood to play the game again both immediately and in future, thus confirming H3a. Comparative feedback, however, decreased the likelihood to immediately play the game again, which disconfirms H3b. Thus, comparative feedback may be less motivating than descriptive or evaluative feedback. Although we did not measure the perception of competition in this game, comparative feedback could elicit perceived social competition in the eyes of players. This perceived social competition may be perceived as more ''controlling'' of player behavior in some way, even though the competition is implied and not explicit. However, this notion warrants further testing. Evaluative feedback was positively related to playing the game in the future compared to descriptive feedback. This is similar to the findings for perceived competence -the evaluation itself may make players want to do better in future. In contrast to Lipkus and Klein (2006) , we did not find any interaction effects for valance and feedback.. In addition to our hypotheses tests, we also found some unexpected results that warrant further investigation. First, age was positively related to playing game in future. This may be perhaps due to the nature of game; brain training is more applicable to older than to younger adults. We also found a different set of effects of feedback on motivation to play the game immediately vs. playing it later. This suggests that current and future plans for games are affected both by motivations for play and intended play time. Although we did not ask people to actually come back and play the game in the future, these findings suggest that further research on free choice future behavior should include a future behavioral measure of future choice behavior in addition to selfreport.. Our study may also serve as the launching point for future research. First, we used a game that was played one time by participants. Future research may investigate the differential role of feedback if players play a game multiple times. In such studies, it would be particularly relevant, if players have played the game a number of game, to base the feedback on players' past performance rather than on a false-feedback manipulation. For instance, descriptive feedback could focus on the completion time compared to past performance (e.g., You have set your third fastest time ever!). Comparative feedback could be given to inform participants if, in general, they are improving (or not). Second, we customized the game Concentration for this experiment. Future research could focus on games that gamers have chosen for themselves to play naturally outside of the experiment.. Third, we used a brain-training game which had potential applications outside the game world. Given that such a game may be different from other educational games or games for behavior change, future work should be replicated using other types of games to determine if a behavior-change framework itself has significant effects on player motivation. Specifically, given our negative effects of competence on play, future research should perhaps particularly examine the effect of competence on skill training games vs. non-skill training games, in order to see if the positive relationship between competence need satisfaction and free choice behavior that we predicted is only endemic to skilltraining games or also applies to other game types.. Finally, we compared three different types of feedback (descriptive, comparative, evaluative) and feedback valence in a false feedback manipulation. Of course, other feedback variables that could be considered in future research include are clarity of the feedback (cf. Ogilvie & Haslett, 1985) , and perceived reliability of (cf. Manser & Muchinsky, 1980) and trust in the feedback source (O'Reilly and Anderson, 1980) . Additionally, using feedback that accurately reflects participants performance rather than experimental condition may be a better predictor of motivation and behavior. Although unlikely, it is possible that the false-feedback manipulation may have made participants suspicious or distrusting of the feedback if they did not agree with the assessment. Therefore, future research should consider participant performance as a casual variable in the feedback to motivation chain, and move away from the false-feedback paradigm towards feedback tailored to actual game play.

O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O B I B O O O O O O O O O O O B O O O B O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O B O B O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O B O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O B O B I O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O B O O O B I O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O B O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O B O O O O O


National research assessment exercises: a comparison of peer review and bibliometrics rankings Development of bibliometric techniques has reached such a level as to suggest their integration or total substitution for classic peer review in the national research assessment exercises, as far as the hard sciences are concerned. In this work we compare rankings lists of universities captured by the first Italian evaluation exercise, through peer review, with the results of bibliometric simulations. The comparison shows the great differences between peer review and bibliometric rankings for excellence and productivity. In recent years there has been unanimous agreement that governments should assign resources for scientific development according to rigorous evaluation criteria. This responds to the needs of the knowledge economy, which demands development of efficient scientific infrastructure capable of supporting the competitiveness of the national production system. The rising costs of research and tight restrictions on budgets add to the tendency for evaluation. Governments thus resort to such exercises, for the following purposes: i) to stimulate greater efficiency in research activity; ii) to allocate resources in function of merit; iii) to reduce information asymmetry between supply and demand for new knowledge; iv) to inform research policies and institutional strategies; and v) to demonstrate that investment in research is effective and delivers public benefits.. The need for evaluation is fully agreed at the theoretical level, but issues are more problematic when it comes to what methods to apply. The recent development of bibliometric techniques has led various governments to introduce bibliometrics, where applicable, in support or substitution for more traditional peer review. In the United Kingdom the Research Excellence Framework (REF), taking place in 2014, is an informed peer-review exercise, where the assessment outcomes will be a product of expert review informed by citation information and other quantitative indicators. It will substitute the previous Research Assessment Exercise series which were pure peerreview. In Italy, the Quality of Research Assessment (VQR), expected in 2012, substitutes the previous pure peer-review Triennial Evaluation Exercise (VTR). It can be considered a hybrid, as the panels of experts can choose one or both of two methodologies for evaluating any particular output: i) citation analysis; and/or ii) peerreview by external experts. The Excellence in Research for Australia initiative (ERA), launched in 2010, is conducted through a pure bibliometric approach for the hard sciences. Single research outputs are evaluated by a citation index referring to world and Australian benchmarks.. The pros and cons of peer-review and bilbiometrics methods have been thoroughly dissected in the literature (Horrobin, 1990; Moxham and Anderson, 1992; MacRoberts and MacRoberts, 1996; Moed, 2002; van Raan, 2005; Pendlebury, 2009; Abramo and D'Angelo, 2011) . For evaluation of individual scientific products, the literature fails to decisively indicate whether one method is better than the other but demonstrates that there is certainly a correlation between the results from peer-review evaluation and those from purely bibliometric exercises. This has been demonstrated for the Italian system based on a broad scale study conducted by Abramo et al. (2009) , with metrics based on the impact factor of journals, and by Franceschet and Costantini (2011) using citation analysis of publications. Preceding studies concerning other nations have also demonstrated a positive correlation between peer quality esteem and citation indicators (Aksnes and Taxt 2004; Oppenheim and Norris 2003; Rinia et al. 1998; Oppenheim 1997) .. The severe limits of peer review emerge when it is applied to comparative evaluation, whether of individuals, research groups or entire institutions. Abramo and D'Angelo (2011) have contrasted the peer-review and bibliometrics approaches in national research assessments and conclude that the bibliometric methodology is by far preferable to peer review in terms of robustness, validity, functionality, time and costs. This is due to the intrinsic limits of all peer-review exercises, in which restrictions on budget and time force the review to focus the evaluation on a limited share of total output from each research organization. One of the consequences is that comparative peer review is limited to the dimension of excellence and is unable to deal with average quality or productivity of the subjects evaluated. A second limitation is that the final rankings are strongly dependent on the share of product evaluated (lack of robustness). A third is that the selection of products to submit to evaluation can be inefficient, due to both technical and social factors (parochialism, the real difficulty of comparing articles from various disciplines, etc.). This can impact negatively on the final rankings and their capacity to represent the true value (or lack of same) for the single organizations evaluated. A fourth consequence is that peer-review evaluations do not offer any assistance to universities in allocating resources to their best individual researchers, since they do not consistently penetrate to precise and comparable levels of information (lack of functionality). Finally, the time and costs of execution involved prevent peerreview evaluations from being sufficiently frequent for effective stimulation of improvement in research systems.. The limitations indicated, particularly those related to the selection and the share of products, lead to legitimate doubts about the accuracy of rankings of organizations as obtained from peer-review national assessment exercises. The aim of this work is to measure the amplitude of shift in rankings of organizations compared to the rankings from bibliometric-type evaluations. Bibliometric simulation is legitimated by the abovenoted correlation between peer review and bibliometrics concerning individual research products. The comparison refers to the first Italian research assessment exercise (VTR, 2006) , for the scientific production from the period 2001-2003. The next section of the work describes the dataset used and the methodology for the analysis. Sections 3 and 4 present and comment on the results obtained from the study, conducted at the aggregate level of disciplines. The last section provides a summary of the main results and some further considerations of the authors. Both within the scientific community and beyond, there is unanimous agreement that resources for science should be assigned according to rigorous evaluation criteria. However there is a lively debate on which methods should be adopted to carry out such evaluations. The peer-review methodology has long been the most common. This was the approach for the first large-scale evaluation in Italy (VTR), dealing with the 2001-2003 triennium and concluded in 2006. Recently, the agency responsible prepared the guidelines for an updated national evaluation (the VQR), this time on the basis of a seven-year period and a more ample set of products, but still a peer-review type exercise.. For whatever evaluation intended to inform a research funding scheme, the conception must be of a manner to achieve the strategic objectives the policy-maker is proposing. For the Italian VTR, the objective was to identify and reward excellence: in this work we have attempted to verify the achievement of the objective. To do this we compared the rankings lists from the VTR with those obtained from evaluation simulations conducted with analogous bibliometric indicators. The analyses have highlighted notable shifts, the causes of which the authors have amply examined in previous works. The results justify very strong doubts about the reliability of the VTR rankings in representing the real excellence of Italian universities, and raise a consequent worry about the choice to distribute part of the ordinary funding for university function on the basis of these rankings. One detailed analysis by the authors shows that the VTR rankings cannot even be correlated with the average productivity of the universities. Everything seems to suggest a reexamination of the choices made for the first VTR and the proposals for the new VQR. The time seems ripe for adoption of a different approach than peer review, at least for the hard sciences, areas where publication in international journals represents a robust proxy of the research output, and where bibliometric techniques offer advantages that are difficult to dispute when compared to peer review. Before showing the comparison between the Italian VTR rankings list 2 and those derived from the bibliometric simulation, we describe the dataset and the specific methodologies applied.

O B I O O O O B I O B O O O O O O O O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O


Medial axis computation for planar free-form shapes Abstract. We present a simple, efficient, and stable method for computing-with any desired precision-the medial axis of simply connected planar domains. The domain boundaries are assumed to be given as polynomial spline curves. Our approach combines known results from the field of geometric approximation theory with a new algorithm from the field of computational geometry. Challenging steps are (1) the approximation of the boundary spline such that the medial axis is geometrically stable, and (2) the efficient decomposition of the domain into base cases where the medial axis can be computed directly and exactly. We solve these problems via spiral biarc approximation and a randomized divide & conquer algorithm. The medial axis has been introduced by H. Blum [5] as a concept for efficient shape description. Meanwhile it has proven useful in many scientific areas, and its fast and stable computation is of vital interest. However, even in R 2 , the task of computing the correct medial axis of a given free-form shape is a highly nontrivial one. See Fig. 1 for a first example.. The efficiency and quality of the axis' computation critically depend on the available boundary representation of the input shape. Algorithms for polygonal boundaries [10, 22, 26, 34] work at satisfactory runtimes, but do not produce stable medial axis approximations for the original shape without expensive pruning. The same is true for point sample representations [4, 6] , which also (and even more) tend to increase the data volume.. On the other hand, implementations that work directly on curved boundaries suffer from high numeric complexity and the arising robustness problems. Also, they usually are inherently slow, as many existing efficient algorithms do not apply to complicated curved objects; see e.g. [13] for a short overview of relevant previous work until 2002. As interest in computing the medial axis has found renewal in recent years, let us briefly further comment on this challenging problem.. There exist two principal problems-apart from stability issues-that need to be addressed when computing a medial axis. One of them is determining the combinatorial structure (i.e., the topology) of the medial axis. This problem has been well solved, Figure 1 : Medial axis of a planar free-form shape. The shape was approximated by 9440 arcs within 0.65 seconds, and the computation of the medial axis took less than 1 second. All computations were done on standard PCs. from both a theoretical and practical point of view, only for point sample and polygonal inputs. For curved boundary objects, most theoretically fast algorithms compute the entire Voronoi diagram, leaving the need of pruning away unwanted and incorrect features. Complicated merging, or insertion, steps have to be performed, depending on whether the algorithm was based on divide & conquer [25, 34] , or on incremental insertion [3, 31] . As such steps process previously computed parts of the medial axis, they are numerically involved and subject to errors if not implemented with care [19] .. Algorithms based on domain decomposition [11] avoid these drawbacks. They lead to a divide & conquer construction [12, 18] as well, but their merging steps are trivial, as effort is shifted to the process of splitting into independent subproblems. In other words, they allow for separating combinatorial calculations from geometric calculations in the medial axis computation. The algorithm we are going to describe in this paper is of this type.. Even when the topology of the medial axis is assumed to be known, the (usually hard) problem of computing its bisectors remains. Quite a lot of work has been devoted to this geometric aspect of the medial axis. See, for example, [17] who focus on rational boundary curves, and [15] where curvature properties are utilized for treating cubic boundary splines. A popular approach is local tracing [14, 32] , where the medial axis is calculated by tracing either the shape boundary or the axis bisectors. In particular, so-called predictor/corrector methods [8, 15] have been proposed for approximating the medial axis in a piecewise manner.. All these approaches described above are rather theoretical work-a practical one is given in [16] . They compute the medial axis by first approximating the boundary spline curve by circular biarcs and then applying the VRONI-package developed by M. Held [22] . VRONI can compute the medial axis of a collection of N points and line segments in (practically) O(N log N ) time; circular arcs are accepted, too, and are converted into a polygonal description. The implemented algorithm is basically incremental insertion, and is capable of constructing the entire Voronoi diagram. Although the computation is done very fast in terms of the input size, N , the resulting two-step approximation [16] blows up the data volume significantly.. 1 Also, no guarantee for the stability of the medial axis approximation can be given.. In the present paper, we describe a simple and fast method that is less data consuming (and thus is efficient also in this sense), and that comes with a stability guarantee. We use an approximation of the shape boundary by biarcs as well, though in a tailored manner. Our algorithm then works directly (and exactly) on shapes bounded by circular arcs. This bears two major advantages: (1) For a fixed accuracy of the approximation, the data volume drops from N to n = O(N 2/3 ) compared to using a polygonal description. (2) The biarc approximation scheme can be tuned to preserve monotonicity of curvature of the original shape, which makes the computed medial axis converge to the exact one. Note that the medial axis of a shape with piecewise circular boundary is composed of conic arcs, and thus has the same analytic complexity as for polygonal domains.. We adopt the shape decomposition approach [11] to achieve simplicity and numerical robustness of the algorithm. As decomposition is by inscribed maximal disks, it is naturally suited to shapes with piecewise circular boundaries. The resulting randomized divide & conquer algorithm runs in expected time O(n log n) if mild assumptions on the graph diameter of the medial axis are met. A high-level description, including a formal runtime and data volume analysis, and a proof of convergence (medial axis stability) are given in [1] . The theoretical foundations being laid, the paper at hands concentrates on practical and experimental aspects of the algorithm.. Section 2 details the method we use for approximating a given polynomial spline curve by spiral biarcs. A careful description of our medial axis algorithm follows in Section 3. Continuing preliminary work in [2] , a variant of the algorithm is worked out that performs the best concerning speed while ensuring robustness in the presence of geometric degeneracies. This includes (but is not restricted to) the proper classification and treatment of base cases, in order to establish correctness and to gain running speed, for both smooth and non-smooth circular boundary spline inputs. Implementation details, experimental data, and selected examples are presented in Section 4. Finally, Section 5 offers some concluding remarks. We have provided an efficient and stable implementation of a medial axis algorithm for planar free-form shapes. To our knowledge, this is the first algorithm that runs fast in practice and at the same time guarantees convergence to the exact medial axis of the input shape. The program can compete with current state-ofthe-art implementations on this field with regard to correctness, speed, and reliability. The basic idea was using a piecewise circular boundary conversion, which allows for appropriate feature preservation of the shape, as well as for a simple and fast medial axis algorithm. An implementation of the algorithm which combines the double and the Gmpq kernels to achieve speed and stability in one single program is possibly an issue for future work. In addition, we plan to the extend the method to multiply connected domains and we will apply the results to obtain efficient methods for the computation of offset curves.

B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O B I I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Interoperable processing of digital elevation models in grid infrastructures Abstract Based on the service specifications of the Open Geospatial Consortium (OGC), Spatial Data Infrastructures (SDI) support the visualization, access to vector and raster data or managing and search for spatial data. A standard for distributed spatial data processing was missing for a long time. This issue was addressed by the development of the OpenGIS Web Processing Service (WPS) specification. However, to process and analyze massive Digital Elevation Models (DEM) computing power and disk memory are scarce commodities. Here we show that Grid Computing in combination with OGC Web Services (OWS) is well suited to accomplish high processing performance and storage capacity for large-scale processing tasks of the geo community. To process these massive amounts of geo-data we develop terrain processing services which are made available as grid services. Our results will be demonstrating how to bridge the gap between the grid world and the OGC world for more sophisticated terrain processing. High quality surfaces models play an important role in a range of geo applications, e.g. disaster management.. Through newly available data acquisition techniques such as Airborne Laserscanner Data (ALS or Light Detection And Ranging, LiDAR) massive high-precision and spatial densly surface data enrich. Therefore high performance computing, storage capacity and datamanagement systems are required due to the increasing volume of available geodata. For example hydrologists have specific requirements for inundation modelling, which is based on very exact surface data and preserve execution of time-consuming and very computationally intensive tasks. For acquiring, analyzing, refining and handling massive terrain data by using traditional desktop Geo-Information-Systems (GIS) rather than capturing account of the advantages of Spatial Data Infrastructure (SDI) or classical SOA-based geoprocessing services could hardly manage this. Anyway there is a need for more sophisticated data processing techniques. The processing of massive data is one of these open questions in GI Sciences (Geo-Information Sciences). Furthermore remote processing tasks and distributed data access should be done in a standardized way. An efficient usage and optimization of current existing IT resources are necessary. This is ensured by using Grid Computing technologies, which are standardized by the Global Grid Forum (GGF). The aim of a Grid system is connecting and accessing distributed data and enabling remote computations execution. This is aspired by developing open Grid infrastructures based on the principles of SOA and the Open Grid Service Architecture (OGSA), which takes the specification of the Web Service Resource Framework (WSRF) into account. In order to connect the GI community with the Grid community the OGC and the OGF (Open Grid Forum) are working together to specify interfaces, best practices and enhance existing Grid Computing and OGC standards. Therefore the OGF signed a memorandum of understanding to collaborate with the OGC. One objective of the collaboration is to integrate the OGC WPS standard with a range of back-end processing environments to enable large-scale processing. In order of particular interest of the Grid community we focus on the OGC WPS standard.. Linking Grid Computing infrastructure to OGC Web Services (OWS) is well suited to accomplish high processing performance and storage capacity and brings GI applications to a higher level raising new questions on service architecture, interfaces and best practices. We develop a variety of terrain processing services based on both OGC and Grid Computing standards. The huge amount of terrain data for Digital Elevation Model (DEM) generation as well as modelling spatial processes based on the DEMs require a vast demand on IT resources. Therefore the terrain processing services (Web Processing Service Terrain Framework) can be integrated as central component into a service architecture that manages and visualizes the 3D data. The potential of this terrain data is increased by the design of services for the preparation, fusion and processing, based on Grid technologies.. Furthermore, in approaching to the INSPIRE directive (passed in 2007) that aims to harmonize geospatial services for data access across Europe, our goal is to support the European environment policy taking the advantages of the benefits of SDI in combination with Grid Computing technologies for other sciences like hydrologists and noise or climate modeler. Additionally, scientists can use standard-conformal access to spatial data using OGC based Web Feature Services (WFS) or Web Coverage Services (WCS) using Grid resources interoperably into the context of INSPIRE SDIs.. The remainder of this contribution is structured as follows: Section "Related works" look forward to recent research works in the SDI and terrain algorithm domain. Section "Linking grid-computing to SDI" introduced to the OGC WPS specification and discusses the basic concepts of the Grid Computing technology and the differences between both. Section "Bridging the gap between OGSA and OWS" demonstrates the architecture and how these two technologies can work together. The next section presents our Web Processing Service Terrain Framework workflow for pre-processing algorithms for DEM and 3D data (section "Terrain web processing services"). We finish with a conclusion and an outlook to further activities. Processing high quality surface based e.g. on LiDAR data requires enormous computing power and storage capacity. To process these vast data sets means to compute million of raw data points and running computationally intensive algorithms. In this paper we have presented a possibility to make the handling of massive surface data easier using Web based geoprocessing services combined with Grid systems. The OGC based Web services as well as the implemented processing algorithms are adapted to the requirements of the Grid architecture. It was shown, which synergies will be result from the connection of Grid Computing technology and SDI standards regarding calculation performance and storage capacity of large terrain data sets and that linking both can be beneficial. Grid Computing offers the capability to outsource processing jobs, whereas SDI provides the framework for processing, accessing and visualize spatial data. In order to orchestrate different processing services, we define a geoprocessing workflow based on the BPEL workflow engine (Fleuren and Müller 2008) .. As proof of concept we processed SRTM terrain data covering Germany. The study area was split in 42 tiles with a size of 128 times 128 km per tile. Therefore about 100 million files (26 GB) were processed with a total processing time for instance of 1,300 CPU hours. Our future work will focus on more tests and qualitative and quantitative analysis of the results.. The officially released OGC WPS standard (version 1.0.0, OGC 2007) provides the basic requirements for real geoprocessing functionalities. This WPS version adopted the IT mainstream by supporting SOAP to exchange XMLbased messages and WSDL to describe Web services. The WPS offers processing resources, so the spectrum of SDI services has been extended considerably, as SDIs are no longer restricted to sharing geo-data only, but also can provide real calculation services. Our terrain pre-processing interfaces are linked to the WPS interface. They may be providing a basis for deriving WPS profiles. Goebel and Zipf (2008) discuss WPS profiles for 3D geoprocessing and try to derive a classification of relevant categories of 3D processes through a top-down approach. On the other hand it is possible considering the basic processes, which may be generic or domain specific-leading to different profiles. The processes defined here may become part of such a future profile. A lot of research and implementations work has been done in GI Sciences and terrain processing field. In this section we give an overview of the state-of-the-art in both domains.

O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O B O O O O O O O O O O O O B I O O O B I I O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O B O O O O O O O O O O O O O O B O O B I O O B I I O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B I O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O B O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O O O B I O O O O O B O O B O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O


A Query Integrator and Manager for the Query Web We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions. In recent years researchers spend as much or more time at their computers as they do in the wet lab. A significant component of this time is spent managing experimental or clinical research data with the aid of spreadsheets, local databases, or small-scale webbased databases [1] [2] [3] [4] . In addition, with the worldwide increase in web-accessible databases at levels ranging from chemicals to public health researchers tend to spend significant time searching these databases for information that is available about, for example, a particular gene, protein or disease of interest. In fact more often than not researchers first consult the online databases before doing experiments, often in order to develop hypotheses that can be tested in the lab.. However, because most databases are designed independently, with different data models and (generally web-based) user interfaces, it has become an increasingly significant effort for researchers to access and combine data from different sources. In a 2002 paper [26] Stein likened the situation to a nation of city-states, with the concomitant problems of lack of standards and methods of access. The immediately obvious solution of a single worldwide repository and/or a universal agreed on set of standards is not compatible with the dynamic nature of biomedical research, and would therefore stifle the innovation that is the source of excitement and progress in the field. In his paper Stein advocated for the use of web services to provide computer-readable access to databases. In the intervening years such services have become increasingly available. However, accessibility is only part of the requirement for data integration. Other requirements include interoperability and an integration architecture.. Interoperability is increasingly being addressed by semantic web technologies, including the linked-data initiative [27] , which in particular has led to the vision of a ''global database'' of linked data and knowledge. However, at the present time most data and knowledge are not yet linked to each other, and although often accessible via web services, are represented and queried in different ways. What is relatively common, however, is that most data can be expressed in some form of XML, which along with JSON [28] , is a common method for exchanging data. Thus, if ways can be found to integrate different forms of XML and/or JSON data then at least part of the vision of the global database can be achieved while waiting for the linked data initiative to become more widespread.. Integration of such XML-based data requires an integration architecture in addition to accessibility and interoperability. Multiple such architectures have been developed over the years; ranging from heavyweight, which require significant effort to set up and maintain, with the gain of increased reliability and quality; to lightweight, which, like web pages, are easy to setup, but may not be easily reusable or shareable [29] .. One heavyweight data integration approach described by Goble and Stevens [29] is called ''view integration''. Views are very popular in relational databases because they permit complex relational tables to be virtually joined into new tables that are more tailored to the interests of particular users. The virtual tables are not actually materialized in the database, but are generated by saved queries. The primary conceptual idea that makes views work is that they exhibit closure, in which the output of the view query is of the same format (a relational table) as the input, thereby permitting views to be treated as source data. SQL queries over the view tables look to the query writer like any other queries; however, at runtime these queries are composed with the view queries to generate queries over the underlying materialized tables. Underneath the hood considerable optimization is employed to make these queries run faster, including caching of the results of running the view queries.. The ''view integration'' approach extends this notion to the Internet, whereby relational tables are analogous to remote sources accessed via web services, and the ''view'' over these sources is a central mediated schema [30] . The mediated schema is associated with a mediator application that reformulates queries over the mediated schema to calls to the remote services, some of which may be ontologies (or the mediated schema may itself be an ontology).. As in relational databases such an approach has appeal because it allows the user to only understand the mediated schema, which may be highly customized to his or her own interests; the mediator does all the work of understanding the potentially large number of source schemas. However, in spite of these potential advantages, most systems have only been deployed in laboratory settings [31, 32] , and there are only a few examples of mediated systems in widespread use [18, 33] because it is very expensive to maintain the mediated schema and to change it as the underlying source schemas change. Thus, like data warehouses, only well-funded groups can support this approach.. In this paper we describe a data integration architecture that attempts to retain the advantages of the view integration approach while remaining lightweight. Like relational views we take advantage of the closure property such that queries generate results that can themselves be queried, thus allowing queries to be treated as views. However, rather than relational tables, the closure is over XML (with potential extensions to JSON as query languages are developed for that representation [34] ). We describe our Query Integrator (QI) application, which permits any user to create, save and reuse a query over any web accessible source(s) in whatever language is most appropriate for the source. The only requirements are that the sources are either in some form of XML or generate some sort of XML as output, and that the results of the saved query also generate some sort of XML. Because of this closure property the saved query can be executed via a (possibly parameterized) REST web service, thereby permitting it to be treated like any other queryable web source, and permitting saved queries to be chained together. The ability to chain queries over other saved queries allows anyone to create an arbitrarily complex view over any number of data sources. Like views over relational data the view can present information to users in ways more tailored to their interests, and that hide unnecessary detail.. Once such queries have been saved they can become available via our REST service as yet another Internet web service that can be included in custom applications that include end-user graphical interfaces, or into a workflow system like Taverna [35] , which allows further processing, analysis and visualization of the results from the query.. The approach we describe is not designed for end-users, who would most likely prefer to fill out forms in a custom application that accesses the saved queries under the hood. Rather, our system is primarily intended for experts in the query languages and sources, who would then make their saved queries available for use by other experts through modifications of saved queries, or by end-user applications that access the saved queries via the REST service.. In the remainder of this paper we describe the architecture of the Query Integrator (QI), illustrate its use for a detailed use case in neuroimaging, show current saved queries that address several additional use cases, and discuss research issues that still need to be resolved. We end by speculating how a Query Web could evolve by linking together multiple instances of the Query Web Integrator and Manager, and how such a query web could be accessed by applications that help end-users more easily integrate data within the rapidly evolving field of biomedicine. In this paper we have described a Query Web Integrator and Manager (QI) that has the potential to tie together many of the currently fragmented web data and knowledge sources through the emergence of an interlinked web of queries. The key ideas that make this possible are the use of XML as the underlying data exchange mechanism, a framework that permits an agnostic view as to the best representation and query language on top of XML, the ability to save and re-use queries, and the ability to treat any saved query as a data source (a view) that can be queried by other queries, either in the same instance or in a separate instance of QI.. Within the context of the classification scheme provided by Goble and Stevens [29] our approach can be primarily characterized as a lightweight view integration method, in that it combines the advantages of views over diverse data sources with the ability for anyone to easily create, edit and save those views such that they are reusable by others. Our approach also has characteristics of other data integration architectures, all within a lightweight framework. That is, a large database stored with a given instance of QI is like a central repository; a view over multiple sources materialized in a given instance of QI is like a warehouse; and distributed instances of QI all running the same query is one type of federated query system.. The ability to chain queries makes QI somewhat similar to workflow systems like Taverna [35] , which allows users to graphically tie external web services together by connecting the output of one service to the input of another, and to save and execute the resulting workflow. Workflows in Taverna, which are represented as XML files, can be saved in a central repository called MyExperiment [69] , where they can re-used by others and included as part of larger workflows. Taverna also provides access to a large number of web services, including over 2200 services registered in the BioCatalogue central registry [70] .. The Taverna toolkit is basically a way to glue together existing web services; unlike QI it does not in itself include methods for creating and saving queries. On the other hand, the availability of the QI REST interface means that queries saved in QI can be included within larger Taverna workflows that might, for example, provide graphical means for entering parameters for template queries, or provide additional processing on the results of queries over multiple sources. In turn, since Taverna workflows are represented in XML QI could potentially provide more sophisticated searching of Taverna workflows and/or available BioCatalogue web services through saved XQueries over the MyExperiment or BioCatalogue REST search services.. QI at its current stage of development is not designed for the enduser, although motivated end-users have used it. Because query languages like SPARQL and XQuery are generally too arcane for most end-users, and because XML output is not easy to read, QI seems best suited at this point for experts in the queries and data sources, who will write and save queries for others to either click on within QI, or more likely, to access via workflows like Taverna or end-user applications like our AnnoteImage program (Section 4.3). This scenario is not that different from current large-scale data management installations where experts are hired to write complex SQL reports, which are then simply clicked on by most users. An advantage of the Query Web approach is that these ''reports'' or ''views'', in the form of saved queries, are available to anyone on the web who has authorization, thus potentially highly leveraging the skills of a few experts for a much larger end-user community. Fig. 15 . Illustration of a type of federated database using multiple instances of QI. Each of several institutions has its own instance of QI, each of which accesses its own XML file (or database) using the same saved query. Each local query in turn accesses a shared ontology web server implemented in a third instance of QI, and a fourth instance of QI joins the results from all the localized queries, thereby removing the need to send large XML data sources over the network and allowing individual institutions to maintain control of their own data.. Of course there are many substantial research issues that need to be addressed before QI can become more widely used. These issues include security, reliability, response time, discoverability, usability, ease of installation and customization among others. Many of these issues have been addressed by others in different contexts, and in future work we will explore the applicability of these methods to QI. However, even in its current state QI has already shown in our own work that it is useful for data integration problems in which security, discoverability and response times are not issues.. The fact that anyone can create queries, as views that can themselves be queried, leads to the potential evolution of a Query Web over the existing document and semantic web. Figs. 3, 12 and 15 each illustrates a small query web, each of which could grow as users write queries that link to these chained queries. Fig. 16 extends this idea, suggesting that users may want to integrate results from smaller query webs with the results from queries over other data and knowledge sources and other query webs. For example, as an extension of the queries generated in Section 3 users may want to integrate genomic, clinical and imaging data in order to search for biomarkers of schizophrenia.. In this way smaller query webs could gradually coalesce into the larger Query Web, with popular queries being accessed often, and less popular or even incorrect queries eventually dying out through disuse. Thus, like the document web and semantic web, the Query Web could evolve organically rather than through monolithic or arbitrary constraints that can never keep up with the rapidly changing nature of biology.. As the research issues become resolved QI should become increasingly useful for ever more diverse users and use cases. But even now we believe it could be of use for similar use cases to those described in this paper. We plan to have an open source downloadable version available soon (through the QI project page [49] ). New installations, together with new services as well as enduser-centered interfaces, should not only be useful at the local level, but could also start to link to one another, thereby initiating the evolution of the Query Web. Such a Query Web has the potential to encompass much more than biomedicine since, like the Web itself, there is nothing in the technology that limits it to a single field. The above components are currently installed on two RedHat Linux server machines in the UW Structural Informatics Group (SIG): xiphoid, which runs an Intel 2.13 Ghz Xeon E5606 (8 M cache) processor with 4G Ram; and axon, which runs an Intel 2.33 Ghz Core2 E6550 (4 M cache) processor with 4G RAM. All components are currently written in Java and deployed on Tomcat [47] , although the web services could be written in any language. The central QI query database is PostgreSQL, and currently contains over 208 queries, all stored as strings. The sizes of the separate service databases are described in Section 4.

O O O O O O O B I O O O O O B I O O O O O O O O O O O O B I O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O 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Mapping Regulations to Industry-Specific Taxonomies For each industry, there exist many taxonomies that are intended for various applications. There are also multiple sources of regulations from different government agencies. Industry practitioners, unlike legal practitioners, are familiar with one or more industry-specific taxonomies but not necessarily regulatory organization systems. To help browsing of regulations by industry practitioners, we propose to map regulations to existing industry-specific taxonomies.A mapping from a single taxonomy to a single regulation is a trivial keyword matching task.From there, we examine techniques to map a single taxonomy to multiple regulations, as well as to map multiple taxonomies to a single regulation. Cosine similarity, Jaccard coefficient and market-basket analysis are tested to model the similarity metric between concepts from different taxonomies. Preliminary evaluations of the three metrics are performed. Examples from the building industry are drawn to illustrate the betterment of regulatory usage from the mapping between various taxonomies and regulations. Government regulations are an important asset of the society. They extend the laws governing the country with specific guidance for corporate and public actions. Ideally regulations should be readily retrievable by interested individuals. To aid understanding of the law, much prior research focused on the abstraction and retrieval of case law [1, 3, 5, 24] , analysis of regulations [15, 16] , and compliance guidance for regulations [12, 13] . Methodologies and tools that enable the browsing of regulations according to industry-specific taxonomies are relatively lacking.. Regulations, like most government information, are organized according to the classification system of the agency rather than the mental models of users [6] . There is a clear need and benefit of traversing regulations using existing industry taxonomies. For instance, in the architectural, engineering and construction (AEC) domain, there are a few ontologies that describe the semantics of building models, such as the CIMsteel Integration Standards (CIS/2) [7] , the Industry Foundation Classes (IFC) [10] , and the OmniClass construction classification system (OmniClass, see Figure 1 ) [22] . These ontologies are all targeted towards the same user group, namely the AEC practitioners, but the structures, vocabularies and coverage differ depending on the application. Most AEC practitioners are familiar with the terms and vocabulary in these ontologies -for them to browse through regulations for compliance requirements, adhering to an existing taxonomy that they are familiar with minimizes learning of new classification and vocabularies. Their mental models are better represented using existing taxonomies rather than agency's classification for regulations.. In this paper, we present a systematic approach to mapping regulations to industry-specific taxonomies. We begin with linking one taxonomy to one regulation which is a trivial keyword extraction task. Extending one taxonomy to multiple regulations requires clustering of relevant sections from different regulations, where we reuse the relatedness analysis core from [15] to compute relevancy between sections. We then discuss the need and the challenges of mapping multiple taxonomies to a single regulation. Three different methodologies are investigated to cluster relevant concepts from different taxonomies in order to map them to one regulation. The natural next step, mapping Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O