i-Dataquest: A heterogeneous information retrieval tool using data graph for the manufacturing industry

2021 ◽  
Vol 132 ◽  
pp. 103527
Author(s):  
Lise Kim ◽  
Esma Yahia ◽  
Frédéric Segonds ◽  
Philippe Véron ◽  
Antoine Mallet
Keyword(s):  
Information Retrieval ◽  
Manufacturing Industry ◽  
Heterogeneous Information ◽  
Data Graph ◽  
Using Data

scholarly journals Similarity analysis of court judgments using clustering of case citation data: a study

2018 ◽  
Vol 7 (2) ◽  
pp. 855
Author(s):  
Disna Davis Kachappilly ◽  
Rupali Sunil Wagh
Keyword(s):  
Information Retrieval ◽  
Data Clustering ◽  
Legal Information ◽  
Knowledge Based ◽  
Retrieval Systems ◽  
Requirements Clustering ◽  
Information Retrieval Systems ◽  
Legal Domain ◽  
Using Data ◽  
Automatic Mechanism

Information retrieval (IR) is an automatic mechanism to extract required information from a collection of unstructured or semi-structured data. IR systems minimize the effort of a user to locate the information based on the requirements. Clustering of documents is carried out as a preprocessing step for filtering irrelevant information in an IR system. Legal domain is a producer as well as consumer of huge in-formation which also contains invaluable legal knowledge and its interpretation. Knowledge based legal information retrieval systems is need of the day. Citation analysis is a technique to find the hidden relationships between the documents and is used for understanding knowledge transfer across various domains and hence becomes very important in legal domain. In this study, similarities among documents are analyzed using data clustering when applied on data of citations in court judgments.

scholarly journals The unreasonable effectiveness of traditional information retrieval in crash report deduplication

2016 ◽  
Author(s):  
Joshua Charles Campbell ◽  
Eddie Antonio Santos ◽  
Abram Hindle
Keyword(s):  
Information Retrieval ◽  
Reporting System ◽  
Software Systems ◽  
Trade Off ◽  
Unreasonable Effectiveness ◽  
Using Data

Organizations like Mozilla, Microsoft, and Apple are flooded with thousands of automated crash reports per day. Although crash reports contain valuable information for debugging, there are often too many for developers to examine individually. Therefore, in industry, crash reports are often automatically grouped together in buckets. Ubuntu’s repository contains crashes from hundreds of software systems available with Ubuntu. A variety of crash report bucketing methods are evaluated using data collected by Ubuntu’s Apport automated crash reporting system. The trade-off between precision and recall of numerous scalable crash 7 deduplication techniques is explored. A set of criteria that a crash deduplication method must meet is presented and several methods that meet these criteria are evaluated on a new dataset. The evaluations presented in this paper show that using off-the-shelf information retrieval techniques, that were not designed to be used with crash reports, outperform other techniques which are specifically designed for the task of crash bucketing at realistic industrial scales. This research indicates that automated crash bucketing still has a lot of room for improvement, especially in terms of identifier tokenization.

A Layered Parameterized Framework for Intelligent Information Retrieval in Dynamic Social Network using Data Mining

2014 ◽  
pp. 231-248
Author(s):  
Shailendra Kumar Sonkar ◽  
Vishal Bhatnagar ◽  
Rama Krishna Challa
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Social Network ◽  
Age Groups ◽  
Dynamic Social Network ◽  
Intelligent Information Retrieval ◽  
Dynamic Social Networks ◽  
Intelligent Information ◽  
Using Data ◽  
Key Parameter

Dynamic social networks contain vast amounts of data, which is changing continuously. A search in a dynamic social network does not guarantee relevant, filtered, and timely information to the users all the time. There should be some sequential processes to apply some techniques and store the information internally that provides the relevant, filtered, and timely information to the users. In this chapter, the authors categorize the social network users into different age groups and identify the suitable and appropriate parameters, then assign these parameters to the already categorized age groups and propose a layered parameterized framework for intelligent information retrieval in dynamic social network using different techniques of data mining. The primary data mining techniques like clustering group the different groups of social network users based on similarities between key parameter items and by classifying the different classes of social network users based on differences among key parameter items, and it can be association rule mining, which finds the frequent social network users from the available users.

scholarly journals ANALISIS KINERJA INDUSTRI MANUFAKTUR DI INDONESIA

2017 ◽  
Vol 17 (1) ◽  
pp. 183
Author(s):  
Etty Puji Lestari ◽  
Isnina WSU
Keyword(s):  
Data Envelopment Analysis ◽  
Market Share ◽  
Manufacturing Industry ◽  
Manufacturing Sector ◽  
Government Policies ◽  
Data Envelopment ◽  
Industry Development ◽  
Industry Sector ◽  
Using Data ◽  
Efficiency And Productivity

The problems on the manufacturing industry in Indonesia, among others, the disparity level of efficiency and productivity of each sub-sector of the manufacturing industry in Indonesian. This occurs due to the imbalance in the structure and dominant market share for some particular type of business in each sub-sector is in the manufacturing sector. The study will analyze the performance of the manufacturing industry in Indonesia using Data Envelopment Analysis. The study states that there is a difference of efficiency at every level of the industry. Therefore, government policies relating to industry development is absolutely necessary to improve the performance of the industry sector.

scholarly journals How Patents Influence Market Value of Industrial Enterprises' Assets?

2021 ◽  
Vol 15 (3) ◽  
pp. 70-78
Author(s):  
Dmitry Dementev
Keyword(s):  
Manufacturing Industry ◽  
Positive Impact ◽  
Market Value ◽  
Electronic Equipment ◽  
Intellectual Activity ◽  
The Sustainable Development ◽  
Innovative Technologies ◽  
Industrial Enterprises ◽  
Actual Application ◽  
Using Data

Scientists and engineers are continuously patenting innovative ideas such as inventions, industrial designs, and utility models. It is therefore relevant to pose the question of the influence of intellectual property in the field of innovative technologies on the market value of the assets of industrial enterprises. We analyse the dependence of the results of intellectual activity in the field of advanced technologies on the capitalization of innovative industrial production after the adoption of the developed technologies. We consider patent landscapes, analyse research publications, and study the dependence of financial indicators on the results of intellectual activity at enterprises producing computers, optics and electronic equipment. Our research methodology is based on the statistical analysis of the dependence of the financial results of industrial enterprises on the actual application of the results of intellectual activity to the technological process. We define the object of analysis by citing research articles and surveys from the WoS database. The patent landscape is assessed using data from commercial information systems such as Orbit Intelligence (Questel) and Amadeus (Bureau van Dijk, Moody’s Analytics) that make it possible to visually show the links between patent activity and technological trends in the computer and electronic technology industries. The research results shall be useful for assessing the effectiveness of employing patents in manufacturing and the prospects of improving production technologies for the formation of corporate innovative technological policy. We conclude that the use of information on patent trends is an effective tool for increasing the competitiveness of enterprises producing electronic equipment. The priority financing of innovative technologies ensures the sustainable development of the manufacturing industry and have a positive impact on the profitability of enterprise assets.

scholarly journals The unreasonable effectiveness of traditional information retrieval in crash report deduplication

2016 ◽  
Author(s):  
Joshua Charles Campbell ◽  
Eddie Antonio Santos ◽  
Abram Hindle
Keyword(s):  
Information Retrieval ◽  
Reporting System ◽  
Software Systems ◽  
Trade Off ◽  
Unreasonable Effectiveness ◽  
Using Data

Organizations like Mozilla, Microsoft, and Apple are flooded with thousands of automated crash reports per day. Although crash reports contain valuable information for debugging, there are often too many for developers to examine individually. Therefore, in industry, crash reports are often automatically grouped together in buckets. Ubuntu’s repository contains crashes from hundreds of software systems available with Ubuntu. A variety of crash report bucketing methods are evaluated using data collected by Ubuntu’s Apport automated crash reporting system. The trade-off between precision and recall of numerous scalable crash 7 deduplication techniques is explored. A set of criteria that a crash deduplication method must meet is presented and several methods that meet these criteria are evaluated on a new dataset. The evaluations presented in this paper show that using off-the-shelf information retrieval techniques, that were not designed to be used with crash reports, outperform other techniques which are specifically designed for the task of crash bucketing at realistic industrial scales. This research indicates that automated crash bucketing still has a lot of room for improvement, especially in terms of identifier tokenization.

scholarly journals Big Data

2013 ◽  
Vol 135 (10) ◽  
pp. 32-37 ◽  
Author(s):  
Ahmed Noor
Keyword(s):  
Big Data ◽  
Manufacturing Industry ◽  
Smart Manufacturing ◽  
New Wave ◽  
New Paradigm ◽  
Management Tools ◽  
Heterogeneous Information ◽  
Large Sets ◽  
Controlled Environments ◽  
And Control

This article reviews the benefits of Big Data in the manufacturing industry as more sophisticated and automated data analytics technologies are being developed. The challenge of Big Data is that it requires management tools to make sense of large sets of heterogeneous information. A new wave of inexpensive electronic sensors, microprocessors, and other components enables more automation in factories, and vast amounts of data to be collected along the way. In automated manufacturing, Big Data can help reduce defects and control costs of products. Smart manufacturing is expected to evolve into the new paradigm of cognitive manufacturing, in which machining and measurements are merged to form more flexible and controlled environments. The article also suggests that the emerging tools being developed to process and manage the Big Data generated by myriads of sensors and other devices can lead to the next scientific, technological, and management revolutions. The revolutions will enable an interconnected, efficient global industrial ecosystem that will fundamentally change how products are invented, manufactured, shipped, and serviced.

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