La preparación profesional del tutor para la dirección del proceso de tutoría

The data shaping technique proposed in Chapter Three works on encoded data. The queries and the input documents need to be encoded. The data encoding incurs a finite overhead and

can have implications of performance. Two approaches are possible. One approach is to reduce the overhead associated with data encoding process. Another approach is to avoid data encoding and use the input as it is. Avoiding data encoding has a disadvantage: the footprint of working memory or working set size (WSS) of the application is likely to increase which may have some performance implication when using GPUs.

Study in Chapter Four suggests several directions for research. One of the directions is to explore design space to address the record linkage problem occurring in unstructured data domain leveraging the research discussed in this chapter. Another research direction is to explore the applicability of similar hash-based algorithms for efficient mining of linked records in semistructured and unstructured data sets using parallel architecture-based processors.

The research in Chapter Five points that changes to structure of trees, for example, orien- tation of subtree, can result in different computation patterns. This idea can be used to design a efficient techniques for tree matching problems in context of unordered trees and for problems involving tree rotations common in bioinformatics.

Deduplication with variable sized chunking (on CPU) incurs a finite overhead which could a bottleneck in performance. The research in Chapter Six suggests a research direction on how to increase throughput of deduplication process. To achieve a high throughput dedupli- cation, for example, wire-rate throughput, we need to address the overhead associated with variables-sized chunking. This can be done designing faster variable-sized chunking algorithms. Another possible approach is to shun the variable-sized chunking and use fized-sized chnking instead. The fixed-sized chunking has one drawback: the fixed-sized chunking techniques lead to reduction in deduplication gains. In order to mitigate the losses incurred in deduplication due to fixed sized chunking we can use smaller chunk sizes.

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