An approximate dynamic programming approach for improving accuracy of lossy data compression by Bloom filters

Bloom filters are a data structure for storing data in a compressed form. They offer excellent space and time efficiency at the cost of some loss of accuracy (so-called lossy compression). This work presents a yes-no Bloom filter, which as a data structure consisting of two parts: the yes-filter which is a standard Bloom filter and the no-filter which is another Bloom filter whose purpose is to represent those objects that were recognised incorrectly by the yes-filter (that is, to recognise the false positives of the yes-filter). By querying the no-filter after an object has been recognised by the yes-filter, we get a chance of rejecting it, which improves the accuracy of data recognition in comparison with the standard Bloom filter of the same total length. A further increase in accuracy is possible if one chooses objects to include in the no-filter so that the no-filter recognises as many as possible false positives but no true positives, thus producing the most accurate yes-no Bloom filter among all yes-no Bloom filters. This paper studies how optimization techniques can be used to maximize the number of false positives recognised by the no-filter, with the constraint being that it should recognise no true positives. To achieve this aim, an Integer Linear Program (ILP) is proposed for the optimal selection of false positives. In practice the problem size is normally large leading to intractable optimal solution. Considering the similarity of the ILP with the Multidimensional Knapsack Problem, an Approximate Dynamic Programming (ADP) model is developed making use of a reduced ILP for the value function approximation. Numerical results show the ADP model works best comparing with a number of heuristics as well as the CPLEX built-in solver (B&B), and this is what can be recommended for use in yes-no Bloom filters. In a wider context of the study of lossy compression algorithms, our researchis an example showing how the arsenal of optimization methods can be applied to improving the accuracy of compressed data.

Impression management and achievement motivation: investigating substantive links

In the present research we investigate impression management (IM) as a substantive personality variable by linking it to differentiated achievement motivation constructs, namely achievement motives (workmastery, competitiveness, fear of failure) and achievement goals (mastery-approach, mastery-avoidance, performance-approach, performance-avoidance). Study 1 revealed that IM was a positive predictor of workmastery and a negative predictor of competitiveness (with and without self-deceptive enhancement (SDE) controlled). Studies 2a and 2b revealed that IM was a positive predictor of mastery-approach goals and mastery-avoidance goals (without and, in Study 2b, with SDE controlled). These findings highlight the value of conceptualizing and utilizing IM as a personality variable in its own right, and shed light on the nature of the achievement motive and achievement goal constructs.