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Survey on the Current Status of Serial and Parallel Algorithms of Frequent Itemset Mining


Frequent itemset mining is one of the fundamental but time-demanding tasks in data mining. It is used to find frequent patterns and generate association rules for these patterns. With the availability of inexpensive storage and progress in data capture technology, the availability of data has reached exa-scale already. But improvements in processor and network technology open up opportunity for parallel and distributed computing to be applied in frequent itemset mining to improve its performance in the light of the challenge of “big data”. Thus, there are challenges in frequency itemset mining to fully harness the parallel processing capability of the computer hardware technologies. Read More

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On the pH and Acid Neutralizing Capacity Profile of Manila Bay Coastal Water Samples in Manila, Philippines


For any body of water, its pH and acid neutralizing capacity (ANC) are two important determinants of its overall condition. The ANC has to be high enough to maintain the pH at an optimal level in order to resist acidification and thereby sustain marine life. An assessment of the pH and ANC of Manila Bay coastal waters along Manila and Pasay, Philippines, was done using 16 collected coastal water samples from four different and equally divided geographic sectors along Roxas Boulevard. The pH of the samples was measured using a pH meter while the ANCs were determined via potentiometric titration against sulfuric acid (H2SO4). Read More