Tuple

About: Tuple is a research topic. Over the lifetime, 6513 publications have been published within this topic receiving 146057 citations. The topic is also known as: tuple & ordered tuplet.

An automated approach to solve simple substitution ciphers

Abstract: This paper reports a method to automatically solve simple substitution ciphers where the message length is about 160 characters and the space character is suppressed during encryption. Patterns available in the crypt are used as opening points. With the help of a dictionary system, words are fitted at these points after suitable filtering. Subsequently, a tuple generator-cum-selector is used to select a meaningful tuple of words for decoding the crypt. During test runs, 70 out of 100 randomly chosen messages were solved to as sufficient a degree as to facilitate manual completion. Manual completion was found to be fairly simple when the algorithm had correctly identified 70 percent or more of the character positions. The time required for the algorithm to reach this stage was, on an average, about twenty minutes on a standard personal computer.

Aggregate Query Answering under Uncertain Schema Mappings

Abstract: Recent interest in managing uncertainty in data integration has led to the introduction of probabilistic schema mappings and the use of probabilistic methods to answer queries across multiple databases using two semantics: by-table and by-tuple. In this paper, we develop three possible semantics for aggregate queries: the range, distribution, and expected value semantics, and show that these three semantics combine with the by-table and by-tuple semantics in six ways. We present algorithms to process COUNT, AVG, SUM, MIN, and MAX queries under all six semantics and develop results on the complexity of processing such queries under all six semantics. We show that computing COUNT is in PTIME for all six semantics and computing SUM is in PTIME for all but the by-tuple/distribution semantics. Finally, we show that AVG, MIN, and MAX are PTIME computable for all by-table semantics and for the by-tuple/range semantics.We developed a prototype implementation and experimented with both real-world traces and simulated data. We show that, as expected, naive processing of aggregates does not scale beyond small databases with a small number of mappings. The results also show that the polynomial time algorithms are scalable up to several million tuples as well as with a large number of mappings.

Knowledge base refinement as improving an incorrect and incomplete domain theory

Abstract: : The ODYSSEUS program automates knowledge base refinement by improving a domain theory. This paper describes the techniques used by ODYSSEUS to address three types of domain theory pathologies: incorrectness, inconsistency, and incompleteness. In ODYSSEUS, an incomplete domain theory is extended by the Metarule Chain Completion Method. This method exploits the use of an explicit meta-level representation of the strategy knowledge for a generic problem class (e.g., heuristic classification) that is separate from the domain theory (e.g., medicine) to be improved. Our work implements and compares the extension of an incompleted domain theory using case-based inductive learning and explanation- based apprenticeship learning; in the latter, learning occurs by completing failed explanations of observed human problem-solving actions. Extending an incomplete domain theory and correcting an incorrect domain theory both use the Confirmation Decision Procedure Method, which validates arbitrary instantiated tuples of the knowledge base by the use of an underlying domain theory. Lastly, the consistency of the knowledge base is improved by use of the Sociopathic Reduction Algorithm. (kr)

Adaptive Load Diffusion for Multiway Windowed Stream Joins

Abstract: In this paper, we present an adaptive load diffusion operator to enable scalable processing of multiway windowed stream joins (MWSJs) using a cluster system. The load diffusion is achieved by a set of novel semantics-pre serving tuple routing algorithms. Different from previous work, the load diffusion operator can (1) preserve the MWSJ semantics while spreading tuples to different hosts for parallel join processing; (2) achieve fine-grained load balancing among distributed hosts; and (3) perform semantics-preserving online adaptations to maintain optimal performance in dynamic stream environments. We have implemented a prototype of the distributed MWSJ framework on top of the System S distributed stream processing system. Our experiment results based on both real data streams and synthetic workloads show that the load diffusion algorithms can efficiently scale-up the performance of MWSJ processing with low overhead.