10,183 research outputs found
Shainin methodology: An alternative or an effective complement to Six Sigma?
Purpose: The purpose of this paper is to provide a brief overview of Six Sigma and Shainin RedX (R) methodology and to propose the modification of Six Sigma methodology in order to achieve the improved efficiency of DMAIC in the diagnostic journey using some of the approaches of Shainin RedX (R) methodology.
Methodology/Approach: The diagnostic journey of Six Sigma has been revised by bringing key elements of Shainin RedX (R) methodology into DMAIC: task domain character of the method, focus on the dominant root-cause, use of the progressive elimination method and the application of a problem-solving strategy.
Findings: This paper presents a proposal of DMAIC framework modification using selected tools and procedures of Shainin RedX (R) methodology in the diagnostic phase.
Research Limitation/implication: Although the improved methodology is used in the environment of the automotive supplier, in this paper, practical examples are not included in order not to violate the licensing rules applied by Shainin LLC.
Originality/Value of paper: The contribution of this article is the proposal of modified methodology, which should improve the effectiveness of problem-solving.Web of Science192311
Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs
Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines
Compressing DNA sequence databases with coil
Background: Publicly available DNA sequence databases such as GenBank are large, and are
growing at an exponential rate. The sheer volume of data being dealt with presents serious storage
and data communications problems. Currently, sequence data is usually kept in large "flat files,"
which are then compressed using standard Lempel-Ziv (gzip) compression – an approach which
rarely achieves good compression ratios. While much research has been done on compressing
individual DNA sequences, surprisingly little has focused on the compression of entire databases
of such sequences. In this study we introduce the sequence database compression software coil.
Results: We have designed and implemented a portable software package, coil, for compressing
and decompressing DNA sequence databases based on the idea of edit-tree coding. coil is geared
towards achieving high compression ratios at the expense of execution time and memory usage
during compression – the compression time represents a "one-off investment" whose cost is
quickly amortised if the resulting compressed file is transmitted many times. Decompression
requires little memory and is extremely fast. We demonstrate a 5% improvement in compression
ratio over state-of-the-art general-purpose compression tools for a large GenBank database file
containing Expressed Sequence Tag (EST) data. Finally, coil can efficiently encode incremental
additions to a sequence database.
Conclusion: coil presents a compelling alternative to conventional compression of flat files for the
storage and distribution of DNA sequence databases having a narrow distribution of sequence
lengths, such as EST data. Increasing compression levels for databases having a wide distribution of
sequence lengths is a direction for future work
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Solving the minimum labelling spanning tree problem using hybrid local search
Given a connected, undirected graph whose edges are labelled (or coloured), the minimum
labelling spanning tree (MLST) problem seeks a spanning tree whose edges have the smallest
number of distinct labels (or colours). In recent work, the MLST problem has been shown
to be NP-hard and some effective heuristics (Modified Genetic Algorithm (MGA) and Pilot
Method (PILOT)) have been proposed and analyzed. A hybrid local search method, that we
call Group-Swap Variable Neighbourhood Search (GS-VNS), is proposed in this paper. It is
obtained by combining two classic metaheuristics: Variable Neighbourhood Search (VNS) and
Simulated Annealing (SA). Computational experiments show that GS-VNS outperforms MGA
and PILOT. Furthermore, a comparison with the results provided by an exact approach shows
that we may quickly obtain optimal or near-optimal solutions with the proposed heuristic
Keyword Search in Relational Databases: Architecture, Approaches and Considerations
Questo lavoro di tesi presenta le diverse soluzioni proposte in letteratura per applicare il paradigma keyword search alle basi di dati relazionali, e vuole delineare una architettura generale per definire e sviluppare questi sistemi. A tal proposito, le soluzioni presentate dalla comunità scientifica sono state analizzate focalizzandosi sui singoli componenti della pipeline di ricerca. Infine, si sono analizzati i processi di valutazione sperimentale di questi sistem
The development and application of metaheuristics for problems in graph theory: A computational study
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.It is known that graph theoretic models have extensive application
to real-life discrete optimization problems. Many of these models
are NP-hard and, as a result, exact methods may be impractical for
large scale problem instances. Consequently, there is a great interest
in developing e±cient approximate methods that yield near-optimal
solutions in acceptable computational times. A class of such methods,
known as metaheuristics, have been proposed with success.
This thesis considers some recently proposed NP-hard combinatorial
optimization problems formulated on graphs. In particular, the min-
imum labelling spanning tree problem, the minimum labelling Steiner
tree problem, and the minimum quartet tree cost problem, are inves-
tigated. Several metaheuristics are proposed for each problem, from
classical approximation algorithms to novel approaches. A compre-
hensive computational investigation in which the proposed methods
are compared with other algorithms recommended in the literature is
reported. The results show that the proposed metaheuristics outper-
form the algorithms recommended in the literature, obtaining optimal
or near-optimal solutions in short computational running times. In
addition, a thorough analysis of the implementation of these methods
provide insights for the implementation of metaheuristic strategies for
other graph theoretic problems
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