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Analysis of warranty based big data has gained considerable attention due to its potential for improving the quality of products whilst minimizing warranty costs. Similarly, customer feedback information and warranty claims, which are commonly stored in warranty databases might be analyzed to improve quality and reliability and reduce costs in areas, including product development processes, advanced product design, and manufacturing. However, three challenges exist, firstly to accurately identify manufacturing faults from these multiple sources of heterogeneous textual data. Secondly, accurately mapping the identified manufacturing faults with the appropriate design information and thirdly, using these mappings to simultaneously optimize costs, design parameters and tolerances. This paper proposes a Decision Support System (DSS) based on novel integrated stepwise methodologies including ontology-based text mining, self-organizing maps, reliability and cost optimization for identifying manufacturing faults, mapping them to design information and finally optimizing design parameters for maximum reliability and minimum cost respectively. The DSS analyses warranty databases which collect the warranty failure information from the customers in a textual format. To extract the hidden knowledge from this, an ontology-based text mining based approach is adopted. A data mining based approach using Self Organizing Maps (SOM) has been proposed to draw information from the warranty database and to relate it to the manufacturing data. The clusters obtained using SOM are analyzed to identify the critical regions, i.e., sections of the map where maximum defects occur. Finally, to facilitate the correct implementation of design parameter changes, the frequency and type of defects analyzed from warranty data are used to identify areas where improvements have resulted in the greatest reliability for the lowest cost.

Evaluating an essay automatically has been an area of active research even though there has been a shift to multiple choice answers in many competitive exams. In this paper, we propose an unsupervised technique to rank essays based on the structural and semantic content of the essays. The approach is unsupervised because it makes use of a the complete set of essays to determine the rank of the an individual essay. We purposely avoid deep parsing and the approach is based on use of both structural features of the essay and also the semantic content of the essay. We evaluate the proposed approach on a set of essays submitted to a competition generated from a single prompt. We compare the ranks of the essays with the ranks given by two different human evaluators. The results show a good correlation between the proposed unsupervised algorithm and the human evaluators. The proposed approach, as designed, is independent of any external knowledge base.

Supervised learning techniques have long been used to analyze unstructured natural language text documents. However, supervised learning techniques are not only computationally intensive but also often require large training corpora. Supervised techniques often fail when such training corpora is either (a) not available or (b) when available, is not statistically significant to enable learning. In many practical scenarios, unsupervised learning techniques become de-facto since the training corpus is not available. In this paper we first describe an unsupervised text analysis technique and demonstrate its usefulness in addressing a real life application to harness ideas from aggregating ideas posted on our company Ideas Portal website.

Merging of search engine results is a key metasearch engine function. Most result merging models try to merge ranked lists of web documents returned by search engines in response to a user query using some linear combination approach. A few give more importance to one search engines as opposed to another based on some performance criteria. Other assign weights to documents ranks etc. However few models compare documents and search engines head to head during the process of result merging. In this paper we propose two models for result merging for metasearch, Fuzzy ANP and Weighted Fuzzy ANP that employ fuzzy linguistic quantifier guided approach to result merging using Saty’s Analytical Network Process. We compare our models to existing result merging models. Our results show significant improvements.

A metasearch engine is an Information Retrieval (IR) system that can query multiple search engines and aggregate ranked list of results returned by them into a single result list of documents, ranked in descending order of relevance to a query. The result aggregation problem have been largely treated as Multi-Criteria Decision Making (MCDM) problem with previous approaches applying simple MCDM techniques such as average, sum, weighted average, weighted sum. Previous research has demonstrated the effectiveness of applying Yager’s Fuzzy Ordered Weighted Average (OWA) operator and its variants in result aggregation. In this paper we propose a result aggregation model based on the Choquet Integral, called Choquet Capacity-Guided Aggregation (CCGA) model which represents an alternative way to aggregate results for metasearch using most equilibrated conditions. We compare the proposed model against existing result aggregation models such as the Borda-Fuse, Weighted Borda-Fuse, OWA and IGOWA.

Evaluating essays automatically has been an area of active research for some time. In this paper, we propose an unsupervised technique to select a set of good essays from a large selection of essays written on the same topic. We use a ‘bag of words’ approach which does not require deep parsing. The approach is based on the content of individual essays and the divergence of the individual essay from the collection when the collection is considered as one large essay. The approach is unsupervised and does not require any reference text to build computational learning model. We evaluate our approach on a set of essays, written by different people, on a single topic submitted to a competition internally within our organization. The approach enables selection of good essays which have a good correlation with the human based selection.

A metasearch engines is a search engine that can be used to query multiple search engines at the same time. Typically a metasearch engine passes a user query to some other search engines, which in turn returns results in the form of ranked result lists. The metasearch engine then aggregates results returned by the search engines into a single ranked result list. Result aggregation is a well studied topic. In this paper we propose a comprehensive model for result merging t-norm Importance Guided Fuzzy Hybrid model (tIGFHM) that considers search engine prior performances during aggregation, uses Saaty's Analytic Hierarchy Process (AHP) to do pair wise comparisons of document and search engines and Yager's t-norm Importance Guided OWA operator to do final result aggregation. Our experiments show that our model performs better than conventional result merging models.

A metasearch engine is a Information Retrieval (IR) tool that greatly expands the scope of Internet Search. Functionally a metasearch engine passes a query to multiple search engines and combines and re-ranks results returned by them in one merged list. Result Merging is a key component of metasearch and different techniques have been applied to solve the problem. In this paper we propose a model for result merging based on Saty's Analytical Network Process (ANP) and Yager's Ordered Weighted Average operator. We compare our results with four existing models for result merging, namely the Borda-Fuse, Weighted Borda-Fuse, Condorcet Fuse and the OWA model.

A metasearch engine queries search engines and collates information returned by them in one result set for the user. Metasearch can be external or internal. In external metasearch, result lists from external, independent search engines are merged. On the other hand, in an internal metasearch, result lists from using different search algorithms on the same corpus are aggregated. Thus result merging is a key function of metasearch. In this work, we propose a model for result merging that is based on the Analytic Hierarchy Process and compares documents and search engines in pair-wise comparison before merging. Our model has the capability to merge result lists based on ranks as well as scores, as returned by search engines. We use the LETOR 2 (LEarning TO Rank) dataset from Microsoft Research Asia for our experiments. When using document ranks, our model improves by 31.60% and 8.58% over the Borda-Fuse and Weighted Borda-Fuse models respectively. When using document scores the improvements are 42.92% and 18.03% respectively.

Metasearch engines provide a mechanism to carry out parallel searches on a number of vertical search systems in parallel. A metasearch engine accepts a user query and passes it to multiple search engines, which in turn return results in the form of ranked result lists. An important task of metasearch is to aggregate results returned by multiple search engines into one composite ranked result list. In this paper we explore the effect of applying algebraic Fuzzy t-norms in result aggregation within a Fuzzy MCDM model which uses Analytic Hierarchy Process (AHP) to do pair wise comparison of search engines and documents prior to result merging using Yager's Ordered Weighted Average OWA. Our proposed model that uses t-norms in aggregation outperforms the OWA model proposed earlier by Diaz et.all and the Hybrid Fuzzy model proposed by De et.all.

A metasearch engines is a tool that can be used to query multiple search engines in parallel. Functionally a metasearch engine passes the user query to multiple search engines, retrieves result lists of documents from them in response and then merges the result lists into one result list for the user. A key function of metasearch engines is to merge results returned by multiple search engines. Here we propose a new model for result merging that is based on Analytic Hierarch Process (AHP) and uses the OWA operator for aggregation. Our model combines the advantages of the pair wise document and search engine analysis provide by the AHP and the fuzzy aggregation provided by the OWA aggregation operator. We compare our Hybrid Fuzzy model with the OWA based model proposed by Diaz.

Result merging of search engine results for metasearch is a well explored area. However most result merging models try to collate document rankings from the search engines whose results are being merged into a single ranking using some mathematical function. However, only a few models compare documents in pair wise comparisons during the process of result merging. In this paper, we propose a Weighted Hybrid Fuzzy Result Merging model that comprehensively compares search engines and documents in pairs before applying the result aggregation function. We compare and contrast the performance of our model with existing models for result merging.

The result merging problem for metasearch engines is to combine multiple result lists, returned by search engines, in response to a query, so as to achieve optimal aggregation. Here we propose three result merging models for metasearch that apply Yager's fuzzy aggregation OWA operator to result merging and extend the OWA model for metasearch proposed by Diaz. These are the importance guided OWA (IGOWA), the algebraic t-norm OWA, and the algebraic t-norm IGOWA models. While the first two are based on Yager's extension of the OWA operator, the third is a combination of the features of the first two. We compare the performance of our models to Diaz's OWA model and the Borda-Fuse and Weighted Borda-Fuse models proposed by Aslam and Montague.

Each search engine queried by a metasearch engine returns results in the form of a result list of documents. The key issue is to combine these lists to achieve the best performance. The salient contribution of this paper is a result merging model that applies Yager's fuzzy aggregation Ordered Weight Average, OWA, operator in combination with the concept of importance guided aggregation to extend the OWA-based result merging model proposed by Diaz. Our result merging model, IGOWA, (Importance Guided OWA) improves upon the OWA model proposed by Diaz so as to allow weights to be applied to search engine result lists. To support our model we also explore a scheme for computing search engine weights. We call the weights obtained from our scheme Query-System Weights and we compare this with the scheme for computing search engine weights proposed by Aslam and Montague. We refer to Aslam's scheme as System Weights.

When a query is passed to multiple search engines, each search engine returns a ranked list of documents. The problem of result merging is to fuse these ranked lists such that optimal performance is achieved as a result of the combination. In this paper, our primary contribution is a result merging method, based on fuzzy set theory that adapts the quantifier-guided, Ordered Weighted Averaging (OWA) operators introduced by Yager. The proposed framework is more comprehensive than the existing OWA operator based method, as our investigation evaluates alternative heuristics for missing documents (those existing exist in some, but not all, ranked lists) in order to place such documents into the ranked lists before merging. It shows that the effectiveness of the merging process is improved over the based strategy known as Borda-fuse.