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Being generous in this context relates to your willingness to pass along what others have tweeted the retweet. If your tweets are all about you all the time, your score drops. If you only retweet others, your score hits 100. While it seems odd that somebody who posts nothing original scores the highest, this metric serves as a function of overall in uence rather than a goal unto itself. Nevertheless, if you re really good at nding great stuff to repeat you will indeed build a brand and grow an audience.

G t tk ;



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Database log replay is the simplest approach to database replication. Taking a page from the filesystem replication playbook, a database logs all changes to permanent structures, typically in an undo or redo log that is used for





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where the tk are Poisson distributed, with a stationary (but possibly time varying) density l t , and G t is the impulse response of some lter. With such a model, it turns out that scaling features or long-range dependence may result from the joint properties of l t and G t . This is especially the case when G t d t and l t is FGN with a nonzero mean. A classical way of revealing the scaling structure of such a ``fractal point process'' amounts to looking at how it departs from an ordinary Poisson process when observed over larger and larger scales. With the ordinary Poisson process, the associated counting process N T is such that Var N T EN T , no matter how large T is, thus guaranteeing the ``Fano factor,'' de ned as F T Var N T ; EN T

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rolling the transaction forward in the event of a system failure Log replay involves manually (or via a scheduled script) copying these log files to another machine, and then reapplying the transactions from the logs to the replica databases using the recovery manager, creating a similar copy of the primary database with some minor changes, including, but not limited to, timestamps and transaction IDs One concern with log-replay-style replication is that it is possible for some transactions to fall into the gutter between the primary and secondary database machines, if the primary crashes and takes its log files with it before the final transactions are copied from the log If you replay the log file, then accept five new transactions in the primary database, then suffer a crash, those five transactions won t make it to the secondary server.

to be constant. This situation contrasts with the fractal case for which we have F T % T 2H 1 ; T 3 I: 2:32

Another concern with log replay is that the database on the remote side is not, in fact, identical to the one on the primary side As a result, it would be more difficult, and, in fact, probably impossible, to take an extract from it and apply that extract back to the primary side, in the event of data corruption As we mentioned previously, timestamps and record identifiers are going to be different on the remote side If it were necessary to copy the replicated database back to the primary database server, serious confusion and corruption will result However, database log replay is a relatively big win because it requires no application changes to implement replication The database administrators can configure the log copy and recovery steps, setting the time interval between copies and the verification of updates on the replicated machines.

We use H here rather than a as the process N t , which has stationary increments, is not LRD but rather asymptotically self-similar. By de nition, the Fano factor is a ratio between uctuations and averages at a given observation scale. As such, it naturally admits a wavelet-based alternative de nition: WF j 2j=2 Var dP j; k ; EaP j; k

Some applications are better suited to this technique than others The following is a brief checklist of features that allow an application to fit the log replay model:.

in which details (resp. approximations) play the role of uctuations (resp. averages). Given this de nition, the scaling relation (2.32) now reads WF j % 2j 2H 1 ; j 3 I;

and moreover we have the explicit equivalence WF Haar j F Allan 2j . Again, this wavelet revisiting of the Fano factor allows for versatile generalizations with increased performance, in which the Haar wavelet is to be replaced by a wavelet with a larger number of vanishing moments. From the above examples, it is worthwhile stressing the fact that wavelets provide the user with a uni ed framework that (1) possesses increased versatility and performance as compared to the earlier methods it generalizes and (2) is equally applicable to continuous processes and point processes.

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