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potential users of Iuriservice. Results therefore concentrate on both sociological variables and IT skills (use of Internet, use of hardware and software applications, use of legal databases, etc.). As regards organizational contexts of users, results show that most newly-recruited judges work under time pressure. Almost 95 % of judges interviewed declared to bring work home in the evening and 87 % added that they worked over the weekends as well. On average, judges work 24 extra hours per week and 63 % of them consider that work pressure is high or very high (see Figure 12.1) With respect to IT skills, although judges typically argue in interviews that they have no time no navigate through the Internet, results indicate the growing use of the Internet among them (only 19 % of them declare not using it). The page of the Of cial Bulletin of the State is the most accessed site (45 % of cases), followed by legal information sites in general (20 %). To the question of which would you like to nd if judges were given a web service system the majority of them proposed a site where doubts regarding professional cases could be shared and discussed (see Figure 12.2). Nevertheless, results also reveal that, despite growing use of the Internet, users of the system will be judges who have medium or low technological abilities, not fully acquainted to new technologies. At the same time, they are willing to accept them, provided they facilitate decision-making and management of daily caseload. The main conclusion relevant to the design of Iuriservice, therefore, is that the web-based platform should be easy to learn and user-friendly for judges.

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Therefore, to employ m-RPDR the system will need to be designed with this load imbalance in mind to guarantee streaming performance.

In this section, we evaluate and compare the multi-row-permutated data reorganization algorithm with the round-robin [2] and the SCADDAR [3] algorithms, both originally proposed for disk arrays The performance metrics used for comparison are data reorganization overhead and streaming load balance The results are computed numerically for a media object of B = 4,000 blocks Unless stated otherwise the system begins with a single node and then incrementally grows to 200 nodes by adding new nodes one by one Each data point is averaged over 50 results obtained from using different random seeds for the random number generator (used in SCADDAR and the m-RPDR algorithms) 15341 Data Reorganization Overhead Data reorganization overhead is de ned as the number of data blocks that need to be redistributed to bring the system back to streaming load balance.

Figure 12.2 Preferences of judges regarding potential web services (2004).