Approximate computing is a technique for bridging the growing imbalance between computational power and computational needs by trading small amounts of accuracy for large amounts of performance or energy. Current approximate computing techniques are configured to choose how to approximate based either on training inputs that are representative of the “real” input or on occasional runtime checks...

In this paper we examine string block edit distance, in which two strings A and B are compared by extracting collections of substrings and placing them into correspondence. This model accounts for certain phenomena encountered in important real-world applications, including pen computing and molecular biology. The basic problem admits a family of variations depending on whether the strings must...

We present a new index for approximate string matching. The index collects text q-samples, that is, disjoint text substrings of length q, at fixed intervals and stores their positions. At search time, part of the text is filtered out by noticing that any occurrence of the pattern must be reflected in the presence of some text q-samples that match approximately inside the pattern. Hence the inde...

A search strategy over encrypted cloud data based on keywords has been improved and has presented a method using different strategies on the client and the server to improve the search efficiency in this paper. The client uses the Chinese and English to achieve the synonym construction of the keywords, the establishment of the fuzzy-syllable words and synonyms set of keywords and the implementa...

Indexing methods for the approximate string matching problem spend a considerable effort generating condensed neighborhoods. Here, we point out that condensed neighborhoods are not a minimal representation of a pattern neighborhood. We show that we can restrict our attention to super condensed neighborhoods which are minimal. We then present an algorithm for generating Super Condensed Neighborh...

Cloud computing involves complex technical and economical systems and interactions. This brings about various challenges, two of which are: (1) debugging and control of computing systems, based on heterogeneous data, and (2) prediction of performance and price of “spot” resources, allocated via auctions. In this paper, we first establish two theoretical results on approximate causal inference. ...

Principal component regression (PCR) is a useful method for regularizing linear regression. Although conceptually simple, straightforward implementations of PCR have high computational costs and so are inappropriate when learning with large scale data. In this paper, we propose efficient algorithms for computing approximate PCR solutions that are, on one hand, high quality approximations to the...

Mutation testing is a well-established approach for evaluating test-suite quality by modifying code using syntaxchanging (and potentially semantics-changing) transformations, called mutation operators. This paper proposes approximate transformations as new mutation operators that can give novel insights about the code and tests. Approximate transformations are semantics-changing transformations...

In this paper, we present three parallel approximate string matching methods on a parallel architecture with heterogeneous workstations to gain supercomputer power at low cost. The first method is the static master-worker with uniform distribution strategy, the second one is the dynamic master-worker with allocation of subtexts and the third one is the dynamic master-worker with allocation of t...

The field of engineering is a creative one. The problems encountered in this field are generally unstructured and imprecise influenced by intuitions and past experiences of a designer. The conventional methods of computing relying on analytical or empirical relations become time consuming and labor intensive when posed with real life problems. To study, model and analyze such problems, approxim...