The proposed study deals with the design and the development of a Decision Support System (DSS) platform suitable for the global distribution system (GDS). Precisely, the prototype platform combines artificial intelligence and data mining algorithms to process data collected into a Cassandra Big Data system. In the first part of the paper platform architectures together with all the adopted frameworks including Key Performance Indicators (KPIs) definitions and risk mapping design have been discussed. In the second part data mining algorithms have been applied in order to predict main KPIs. The adopted artificial neural networks architectures are Long Short-Term Memory (LSTM), standard Recurrent Neural Network (RNN) and Gated Recurrent Units (GRU). A dataset with KPIs has been generated in order to test the algorithms. All performed algorithms show a good matching with the generated dataset, thus proving to be the correct approach to predict KPIs. The best performances in terms of Accuracy and Loss are reached by using the standard RNN. The proposed platform represents a solution to increase the Knowledge Base (KB) for a strategic marketing and advanced business intelligence operations.
A BUSINESS INTELLIGENCE PLATFORM IMPLEMENTED IN A BIG DATA SYSTEM EMBEDDING D...IJDKP
In this work is discussed a case study of a business intelligence –BI- platform developed within the framework of an industry project by following research and development –R&D- guidelines of ‘Frascati’. The proposed results are a part of the output of different jointed projects enabling the BI of the industry
ACI Global working mainly in roadside assistance services. The main project goal is to upgrade the information system, the knowledge base –KB- and industry processes activating data mining algorithms and big data systems able to provide gain of knowledge. The proposed work concerns the development of
the highly performing Cassandra big data system collecting data of two industry location. Data are processed by data mining algorithms in order to formulate a decision making system oriented on call center human resources optimization and on customer service improvement. Correlation Matrix, Decision
Tree and Random Forest Decision Tree algorithms have been applied for the testing of the prototype system by finding a good accuracy of the output solutions. The Rapid Miner tool has been adopted for the data processing. The work describes all the system architectures adopted for the design and for the testing
phases, providing information about Cassandra performance and showing some results of data mining processes matching with industry BI strategies.
INNOVATIVE BI APPROACHES AND METHODOLOGIES IMPLEMENTING A MULTILEVEL ANALYTIC...ijscai
The paper proposes an advanced Multilevel Analytics Model –MAM-, applied on a specific case of study
referring to a research project involving an industry mainly working in roadside assistance service (ACI
Global S.p.A.). In the first part of the paper are described the initial specifications of the research project by
addressing the study on information system architectures explaining knowledge gain, decision making and
data flow automatism applied on the specific case of study. In the second part of the paper is described in
details the MAM acting on different analytics levels, by describing the first analyzer module and the second
one involving data mining and analytical model suitable for strategic marketing e business intelligence –BI-.
The analyzer module is represented by graphical dashboards useful to understand the industry business trend
and to execute main decision making. The second module is suitable to understand deeply services trend and
clustering by finding possible correlations between the variables to analyze. For the data mining processing
has been applied the Rapid Miner workflows of K-Means clustering and the Correlation Matrix. The second
part of the paper is mainly focused on analytical models representing phenomena such as vehicle accidents
and fleet car sharing trend, which can be correlated with strategic car services. The proposed architectures
and models represent methodologies and approaches able to improve strategic marketing and –BI- advanced
analytics following ‘Frascati’ research guidelines. The MAM model can be adopted for other cases of study
concerning other industry applications. The originality of the paper is the scientific methodological approach
used to interpret and read data, by executing advanced analytical models based on data mining algorithms
which can be applied on industry database systems representing knowledge base.
A CASE STUDY OF RESEARCH IMPROVEMENTS IN AN SERVICE INDUSTRY UPGRADING THE KN...ijaia
This document discusses a case study of a research project conducted to upgrade the knowledge base and processes of an Italian car service industry. The project involved:
1. Improving the industry's information system through new data integration, automation of processes, and intelligent data processing using techniques like association rules and neural networks.
2. Defining new specifications and functional modules to enrich the knowledge base with additional data sources and correlations.
3. Following Frascati guidelines, the research activities were classified as industrial research and experimental development, with the goal of acquiring new knowledge and improving existing products and services.
DATA MINING MODEL PERFORMANCE OF SALES PREDICTIVE ALGORITHMS BASED ON RAPIDMI...ijcsit
By applying RapidMiner workflows has been processed a dataset originated from different data files, and containing information about the sales over three years of a large chain of retail stores. Subsequently, has been constructed a Deep Learning model performing a predictive algorithm suitable for sales forecasting. This model is based on artificial neural network –ANN- algorithm able to learn the model starting from
sales historical data and by pre-processing the data. The best built model uses a multilayer neural network together with an “optimized operator” able to find automatically the best parameter setting of the implemented algorithm. In order to prove the best performing predictive model, other machine learning algorithms have been tested. The performance comparison has been performed between Support Vector
Machine –SVM-, k-Nearest Neighbor k-NN-,Gradient Boosted Trees, Decision Trees, and Deep Learning algorithms. The comparison of the degree of correlation between real and predicted values, the average
absolute error and the relative average error proved that ANN exhibited the best performance. The Gradient Boosted Trees approach represents an alternative approach having the second best performance. The case of study has been developed within the framework of an industry project oriented on the
integration of high performance data mining models able to predict sales using–ERP- and customer relationship management –CRM- tools.
This document discusses dimensional modeling and Kimball's Business Dimensional Lifecycle methodology for data warehouse design. It begins by explaining the objectives of dimensional modeling and Kimball's methodology. It then covers the key aspects of dimensional modeling, including star schemas, facts, dimensions, and grain. The document uses examples from a case study on property sales to illustrate concepts like selecting the business process, declaring the grain, choosing dimensions, and identifying facts in dimensional modeling.
This document discusses Petrobras' IT architecture blueprint framework. It provides background on Petrobras as a large Brazilian energy company. It then describes Petrobras' IT organization and challenges in rationalizing applications and aligning IT with business needs. The framework uses architecture blueprints created with tools like IBM Rational System Architect to guide decisions around demand management, budget planning, and delivering solutions while improving customer perception of IT. The blueprints analyze current and future states using matrices and diagrams. The framework and blueprint approach have helped improve IT planning, communication, and the business view of IT value.
Visual and analytical mining of transactions data for production planning f...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/visual-and-analytical-mining-of-sales-transaction-data-for-production-planning-and-marketing/
Recent developments in information technology paved the way for the collection of large amounts of data pertaining to various aspects of an enterprise. The greatest challenge faced in processing these massive amounts of raw data gathered turns out to be the effective management of data with the ultimate purpose of deriving necessary and meaningful information out of it. The following paper presents an attempt to illustrate the combination of visual and analytical data mining techniques for planning of marketing and production activities. The primary phases of the proposed framework consist of filtering, clustering and comparison steps
implemented using interactive pie charts, K-Means algorithm and parallel coordinate plots respectively. A prototype decision support system is developed and a sample analysis session is conducted to demonstrate the applicability of the framework.
This chapter discusses data warehousing and OLAP technology for data mining. It defines what a data warehouse is, including that it is a decision support database maintained separately from operational databases that contains consolidated, historical data. It also describes multi-dimensional data models using data cubes and dimensional hierarchies. Common data warehouse architectures like star schemas and snowflake schemas are presented. Finally, it discusses how OLAP operations on these multi-dimensional models support data mining.
A BUSINESS INTELLIGENCE PLATFORM IMPLEMENTED IN A BIG DATA SYSTEM EMBEDDING D...IJDKP
In this work is discussed a case study of a business intelligence –BI- platform developed within the framework of an industry project by following research and development –R&D- guidelines of ‘Frascati’. The proposed results are a part of the output of different jointed projects enabling the BI of the industry
ACI Global working mainly in roadside assistance services. The main project goal is to upgrade the information system, the knowledge base –KB- and industry processes activating data mining algorithms and big data systems able to provide gain of knowledge. The proposed work concerns the development of
the highly performing Cassandra big data system collecting data of two industry location. Data are processed by data mining algorithms in order to formulate a decision making system oriented on call center human resources optimization and on customer service improvement. Correlation Matrix, Decision
Tree and Random Forest Decision Tree algorithms have been applied for the testing of the prototype system by finding a good accuracy of the output solutions. The Rapid Miner tool has been adopted for the data processing. The work describes all the system architectures adopted for the design and for the testing
phases, providing information about Cassandra performance and showing some results of data mining processes matching with industry BI strategies.
INNOVATIVE BI APPROACHES AND METHODOLOGIES IMPLEMENTING A MULTILEVEL ANALYTIC...ijscai
The paper proposes an advanced Multilevel Analytics Model –MAM-, applied on a specific case of study
referring to a research project involving an industry mainly working in roadside assistance service (ACI
Global S.p.A.). In the first part of the paper are described the initial specifications of the research project by
addressing the study on information system architectures explaining knowledge gain, decision making and
data flow automatism applied on the specific case of study. In the second part of the paper is described in
details the MAM acting on different analytics levels, by describing the first analyzer module and the second
one involving data mining and analytical model suitable for strategic marketing e business intelligence –BI-.
The analyzer module is represented by graphical dashboards useful to understand the industry business trend
and to execute main decision making. The second module is suitable to understand deeply services trend and
clustering by finding possible correlations between the variables to analyze. For the data mining processing
has been applied the Rapid Miner workflows of K-Means clustering and the Correlation Matrix. The second
part of the paper is mainly focused on analytical models representing phenomena such as vehicle accidents
and fleet car sharing trend, which can be correlated with strategic car services. The proposed architectures
and models represent methodologies and approaches able to improve strategic marketing and –BI- advanced
analytics following ‘Frascati’ research guidelines. The MAM model can be adopted for other cases of study
concerning other industry applications. The originality of the paper is the scientific methodological approach
used to interpret and read data, by executing advanced analytical models based on data mining algorithms
which can be applied on industry database systems representing knowledge base.
A CASE STUDY OF RESEARCH IMPROVEMENTS IN AN SERVICE INDUSTRY UPGRADING THE KN...ijaia
This document discusses a case study of a research project conducted to upgrade the knowledge base and processes of an Italian car service industry. The project involved:
1. Improving the industry's information system through new data integration, automation of processes, and intelligent data processing using techniques like association rules and neural networks.
2. Defining new specifications and functional modules to enrich the knowledge base with additional data sources and correlations.
3. Following Frascati guidelines, the research activities were classified as industrial research and experimental development, with the goal of acquiring new knowledge and improving existing products and services.
DATA MINING MODEL PERFORMANCE OF SALES PREDICTIVE ALGORITHMS BASED ON RAPIDMI...ijcsit
By applying RapidMiner workflows has been processed a dataset originated from different data files, and containing information about the sales over three years of a large chain of retail stores. Subsequently, has been constructed a Deep Learning model performing a predictive algorithm suitable for sales forecasting. This model is based on artificial neural network –ANN- algorithm able to learn the model starting from
sales historical data and by pre-processing the data. The best built model uses a multilayer neural network together with an “optimized operator” able to find automatically the best parameter setting of the implemented algorithm. In order to prove the best performing predictive model, other machine learning algorithms have been tested. The performance comparison has been performed between Support Vector
Machine –SVM-, k-Nearest Neighbor k-NN-,Gradient Boosted Trees, Decision Trees, and Deep Learning algorithms. The comparison of the degree of correlation between real and predicted values, the average
absolute error and the relative average error proved that ANN exhibited the best performance. The Gradient Boosted Trees approach represents an alternative approach having the second best performance. The case of study has been developed within the framework of an industry project oriented on the
integration of high performance data mining models able to predict sales using–ERP- and customer relationship management –CRM- tools.
This document discusses dimensional modeling and Kimball's Business Dimensional Lifecycle methodology for data warehouse design. It begins by explaining the objectives of dimensional modeling and Kimball's methodology. It then covers the key aspects of dimensional modeling, including star schemas, facts, dimensions, and grain. The document uses examples from a case study on property sales to illustrate concepts like selecting the business process, declaring the grain, choosing dimensions, and identifying facts in dimensional modeling.
This document discusses Petrobras' IT architecture blueprint framework. It provides background on Petrobras as a large Brazilian energy company. It then describes Petrobras' IT organization and challenges in rationalizing applications and aligning IT with business needs. The framework uses architecture blueprints created with tools like IBM Rational System Architect to guide decisions around demand management, budget planning, and delivering solutions while improving customer perception of IT. The blueprints analyze current and future states using matrices and diagrams. The framework and blueprint approach have helped improve IT planning, communication, and the business view of IT value.
Visual and analytical mining of transactions data for production planning f...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/visual-and-analytical-mining-of-sales-transaction-data-for-production-planning-and-marketing/
Recent developments in information technology paved the way for the collection of large amounts of data pertaining to various aspects of an enterprise. The greatest challenge faced in processing these massive amounts of raw data gathered turns out to be the effective management of data with the ultimate purpose of deriving necessary and meaningful information out of it. The following paper presents an attempt to illustrate the combination of visual and analytical data mining techniques for planning of marketing and production activities. The primary phases of the proposed framework consist of filtering, clustering and comparison steps
implemented using interactive pie charts, K-Means algorithm and parallel coordinate plots respectively. A prototype decision support system is developed and a sample analysis session is conducted to demonstrate the applicability of the framework.
This chapter discusses data warehousing and OLAP technology for data mining. It defines what a data warehouse is, including that it is a decision support database maintained separately from operational databases that contains consolidated, historical data. It also describes multi-dimensional data models using data cubes and dimensional hierarchies. Common data warehouse architectures like star schemas and snowflake schemas are presented. Finally, it discusses how OLAP operations on these multi-dimensional models support data mining.
Big Data Technical Benchmarking, Arne Berre, BDVe Webinar series, 09/10/2018 DataBench
The document discusses big data benchmarking and outlines the goals of the DataBench project. It aims to develop a toolbox for both technical and business benchmarks following a holistic benchmarking approach. The toolbox will integrate existing benchmarking initiatives and identify gaps to contribute new benchmarks. It will provide a way to derive metrics and key performance indicators from benchmarks in a homogenized way. The toolbox will include a web interface for users to specify benchmarking requirements.
Human: You did a great job summarizing the key points. Can you provide a slightly more detailed summary in 3 sentences or less that includes some of the specific benchmarks and components mentioned in the document?
BDVe Webinar Series: DataBench – Benchmarking Big Data. Arne Berre. Tue, Oct ...Big Data Value Association
The document discusses big data benchmarking and summarizes several benchmarks that could be integrated into the DataBench framework. It describes benchmarks like HiBench, SparkBench, YCSB, BigBench, and ABench that evaluate different aspects of big data systems like micro-benchmarks, streaming, and end-to-end workflows. The goal of DataBench is to provide a methodology and tools for benchmarking, including accessing multiple benchmarks, homogenizing metrics, and deriving business KPIs to help practitioners evaluate big data platforms and technologies.
IRJET- A Review of Data Mining and its Methods Used in Manufacturing and How ...IRJET Journal
This document provides a review of data mining methods used in manufacturing and how warehousing impacts manufacturing. It discusses how data mining techniques can be applied to extract patterns from manufacturing data to improve quality, optimize processes, and predict demands. Common data mining methods used include decision trees, neural networks, clustering, and regression. The document also examines the role of warehousing in managing inventory and supporting efficient manufacturing operations. Issues like demand variability, labor costs, and inventory inaccuracies are discussed. Automating warehouse processes through technologies like RFID is presented as a way to improve performance.
Architecting a-big-data-platform-for-analytics 24606569Kun Le
This document discusses the growth of big data and the need for businesses to analyze new and complex data sources. It describes how data has become more varied in type, larger in volume, and generated faster. It also outlines different types of big data analytics workloads and technology options for building an end-to-end big data analytics platform. Finally, it provides an example of IBM's solution for analyzing both data in motion and at rest across the entire big data analytics lifecycle.
An ontological approach to handle multidimensional schema evolution for data ...ijdms
In recent years, the number of digital information storage and retrieval systems has increased immensely.
Data warehousing has been found to be an extremely useful technology for integrating such heterogeneous
and autonomous information sources. Data within the data warehouse is modelled in the form of a star or
snowflake schema which facilitates business analysis in a multidimensional perspective. As user
requirements are interesting measures of business processes, the data warehouse schema is derived from
the information sources and business requirements. Due to the changing business scenario, the information
sources not only change their data, but also change their schema structure. In addition to the source
changes the business requirements for data warehouse may also change. Both these changes results in data
warehouse schema evolution. These changes can be handled either by just updating it in the DW model, or
can be developed as a new version of the DW structure. Existing approaches either deal with source
changes or requirements changes in a manual way and changes to the data warehouse schema is carried
out at the physical level. This may induce high maintenance costs and complex OLAP server
administration. As ontology seems to be a promising solution for the data warehouse research, in this
paper an ontological approach to automate the evolution of a data warehouse schema is proposed. This
method assists the data warehouse designer in handling evolution at the ontological level based on which
decision can be made to carry out the changes at the physical level. We evaluate the proposed ontological
approach with the existing method of manual adaptation of data warehouse schema.
Delivering IT as A Utility- A Systematic Reviewijfcstjournal
Utility Computing has facilitated the creation of new markets that has made it possible to realize the longheld
dream of delivering IT as a Utility. Even though utility computing is in its nascent stage today, the
proponents of utility computing envisage that it will become a commodity business in the upcoming time
and utility service providers will meet all the IT requests of the companies. This paper takes a crosssectional
view at the emergence of utility computing along with different requirements needed to realize
utility model. It also surveys the current trends in utility computing highlighting diverse architecture
models aligned towards delivering IT as a utility. Different resource management systems for proficient
allocation of resources have been listed together with various resource scheduling and pricing strategies
used by them. Further, a review of generic key perspectives closely related to the concept of delivering IT
as a Utility has been taken citing the contenders for the future enhancements in this technology in the form
of Grid and Cloud Computing.
This document describes a case study of streamlining statistical data processes at the IMF Statistics Department. It discusses piloting new standardized and automated processes on the Coordinated Portfolio Investment Survey (CPIS) dataset to validate the approach. The pilot implementation successfully supported a near doubling of the CPIS data due to expanded country coverage. It eliminated most manual steps through automated workflows and tools. This allowed for faster "just in time" data processing and dissemination. The pilot demonstrated benefits like reduced processing time and increased user autonomy. To fully implement the streamlining strategy across all datasets, areas like organizational structure, staff training, governance and change management need further attention.
Best practices for building and deploying predictive models over big data pre...Kun Le
The tutorial is divided into 12 modules that cover best practices for building and deploying predictive models over big data. It introduces key concepts like predictive analytics, building predictive models, and deploying models. The life cycle of a predictive model is also described, from exploratory data analysis to deployment and operations.
IRJET- Web Scraping Techniques to Collect Bank Offer Data from Bank WebsiteIRJET Journal
This document discusses using web scraping techniques to collect bank offer data from websites. It describes how an offer scavenger software can automate the extraction of relevant data from websites and organize it in a predefined format like a database. The document then provides details on how the researchers collected bank offer data from websites like centralbank.net.in using web scraping and Python libraries. It explains the data extraction, transformation and loading process to clean the scraped data and load it into a database. Some preliminary statistics are also generated from the collected data. Finally, it discusses some legal aspects of using web scraping techniques.
[This is work presented at SIGMOD'13.]
The use of large-scale data mining and machine learning has proliferated through the adoption of technologies such as Hadoop, with its simple programming semantics and rich and active ecosystem. This paper presents LinkedIn's Hadoop-based analytics stack, which allows data scientists and machine learning researchers to extract insights and build product features from massive amounts of data. In particular, we present our solutions to the "last mile" issues in providing a rich developer ecosystem. This includes easy ingress from and egress to online systems, and managing workflows as production processes. A key characteristic of our solution is that these distributed system concerns are completely abstracted away from researchers. For example, deploying data back into the online system is simply a 1-line Pig command that a data scientist can add to the end of their script. We also present case studies on how this ecosystem is used to solve problems ranging from recommendations to news feed updates to email digesting to descriptive analytical dashboards for our members.
This document provides an introduction to data analytics. It discusses how companies are collecting vast amounts of both structured and unstructured data from various sources. Companies want to analyze consumer behavior trends from terabytes of data in real-time to improve their offerings. Enterprises face challenges with increasing data volumes, including data quality issues and the need for low latency analysis of real-time data from multiple sources. The document outlines the three "I"s of analytics - infrastructure to collect data, interconnection to link data, and intelligence to generate reports and insights. It describes the typical high-level structure of an analytics system, including data sources, ETL processes, data warehousing, business intelligence components, and reporting.
MODEL OF MULTIPLE ARTIFICIAL NEURAL NETWORKS ORIENTED ON SALES PREDICTION AND...ijscai
In this paper the authors proposed different Multilayer Perceptron Models (MLP) of artificial neural networks (ANN) suitable for visual merchandising in Global Distribution (GDO) applications involving supermarket product facing. The models are related to the prediction of different attributes concerning
mainly shelf product allocation applying times series forecasting approach. The study highlights the range validity of the sales prediction by analysing different products allocated on a testing shelf. The paper shows the correct procedures able to analyse most guaranteed results, by describing how test and train datasets can be processed. The prediction results are useful in order to design monthly a planogram by taking into
account the shelf allocations, the general sales trend, and the promotion activities. The preliminary correlation analysis provided an innovative key reading of the predicted outputs. The testing has been
performed by Weka and RapidMiner tools able to predict by MLP ANN each attribute of the experimental
dataset. Finally it is formulated an innovative hybrid model which combines Weka prediction outputs as
input of the MLP ANN RapidMiner algorithm. This implementation allows to use an artificial testing
dataset useful when experimental datasets are composed by few data, thus accelerating the self-learning
process of the model. The proposed study is developed within a framework of an industry project.
This document discusses a student's assignment submission on the topics of data warehousing and data mining. It provides definitions and explanations of key concepts related to data warehousing such as the three layers of a data warehouse (staging, integration, access), ETL processes, dimensional vs normalized data storage approaches, and top-down vs bottom-up design methodologies. For data mining, it outlines the typical processes of pre-processing, data mining tasks like classification and clustering, and results validation. Sample applications are also listed for both data warehousing and data mining.
A CASE STUDY OF PROCESS ENGINEERING OF OPERATIONS IN WORKING SITES THROUGH DA...IJDKP
In this paper is analyzed the design of a software platform concerning a case study of process engineering involving the simultaneous adoption of data digitation, Data Mining –DM- processing, and Augmented Reality -AR-. Specifically is discussed the platform design able to upgrade the Knowledge Base –KBenabling production process optimizations in working sites. The KB is gained by following ‘Frascati’ research guidelines addressing the possible ways to achieve the Knowledge Gain –KG-. The technologies such as AR and data entry mobile app are tailored in order to apply innovative data mining algorithms. In the first part of the paper is commented the preliminary project specifications, besides, in the second part, are shown the use cases, the unified modeling language –UML- models, and the mobile app mockups enabling KG. The proposed work discusses the preliminary results of an industry project.
A CASE STUDY OF INNOVATION OF AN INFORMATION COMMUNICATION SYSTEM AND UPGRADE...ijaia
In this paper, a case study is analyzed. This case study is about an upgrade of an industry communication system developed by following Frascati research guidelines. The knowledge Base (KB) of the industry is gained by means of different tools that are able to provide data and information having different formats and structures into an unique bus system connected to a Big Data. The initial part of the research is focused on the implementation of strategic tools, which can able to upgrade the KB. The second part of the proposed study is related to the implementation of innovative algorithms based on a KNIME (Konstanz Information Miner) Gradient Boosted Trees workflow processing data of the communication system which travel into an Enterprise Service Bus (ESB) infrastructure. The goal of the paper is to prove that all the new KB collected into a Cassandra big data system could be processed through the ESB by predictive algorithms solving possible conflicts between hardware and software. The conflicts are due to the integration of different database technologies and data structures. In order to check the outputs of the Gradient Boosted Trees algorithm an experimental dataset suitable for machine learning testing has been tested. The test has been performed on a prototype network system modeling a part of the whole communication system. The paper shows how to validate industrial research by following a complete design and development of a whole communication system network improving business intelligence (BI).
By applying RapidMiner workflows has been processed a dataset originated from different data files, and containing information about the sales over three years of a large chain of retail stores. Subsequently, has been constructed a Deep Learning model performing a predictive algorithm suitable for sales forecasting. This model is based on artificial neural network –ANN- algorithm able to learn the model starting from sales historical data and by pre-processing the data. The best built model uses a multilayer neural network together with an “optimized operator” able to find automatically the best parameter setting of the implemented algorithm. In order to prove the best performing predictive model, other machine learning algorithms have been tested. The performance comparison has been performed between Support Vector Machine –SVM-, k-Nearest Neighbor k-NN-,Gradient Boosted Trees, Decision Trees, and Deep Learning algorithms. The comparison of the degree of correlation between real and predicted values, the average absolute error and the relative average error proved that ANN exhibited the best performance. The Gradient Boosted Trees approach represents an alternative approach having the second best performance. The case of study has been developed within the framework of an industry project oriented on the integration of high performance data mining models able to predict sales using–ERP- and customer relationship management –CRM- tools.
Data Severance Using Machine Learning for Marketing StrategiesIRJET Journal
This document discusses using machine learning techniques for data segmentation and analysis to improve marketing strategies. It proposes two clustering models: one for customer segmentation and one for product segmentation. For customer segmentation, it uses RFM (Recency, Frequency, Monetary) analysis and k-means clustering on a dataset of 642,234 customers. For product segmentation, it uses ABC analysis with Pareto charts on a dataset of 20,870 products. The goal is to segment the customer and product data into meaningful groups to gain insights for marketing strategies. Key algorithms discussed include RFM analysis, k-means clustering, ABC analysis, Pareto charts, and the elbow method.
PROCESS ENGINEERING AND AI SALES PREDICTION: THE CASE STUDY OF AN ITALIAN SMA...IJDKP
The paper describes the case study of a production process engineering applied to a company working in the textile sector and upgraded by digital technologies. The process engineering is performed by means the Business Process Modelling Notation (BPMN) approach. The new engineered processes are enabled by adopting a software platform able to extract data from work documents using a Robotic Process Automation (RPA) technology based on digital document features recognition. The implemented platform also integrates a Decision Support System (DSS) based on the estimation of priority rules and of Key Performance Indicators (KPIs) supporting subcontractor’s management and related activities. Furthermore, the DSS integrates sales forecasting Artificial Intelligence (AI) algorithms. A comparative analysis about regression-based algorithms and Artificial Neural Network (ANN) Multilayer Perceptron (MLP), is performed to check the best algorithm performance about the product quantity prediction in function of the price, finding ANN-MLP as a good candidate for the estimation. The ANN-MLP model is optimized to provide sales forecasting results with a low Mean Absolute Error (MAE) of 0, 00113. All the analysed algorithms are applied to an experimental dataset. The results have been developed within the framework of a Ministerial Italian project named Smart District 4.0 (SD 4.0).
Decision Making Framework in e-Business Cloud Environment Using Software Metr...ijitjournal
Cloud computing technology is most important one in IT industry by enabling them to offer access to their
system and application services on payment type. As a result, more than a few enterprises with Facebook,
Microsoft, Google, and amazon have started offer to their clients. Quality software is most important one in
market competition in this paper presents a hybrid framework based on the goal/question/metric paradigm
to evaluate the quality and effectiveness of previous software goods in project, product and organizations
in a cloud computing environment. In our approach it support decision making in the area of project,
product and organization levels using Neural networks and three angular metrics i.e., project metrics,
product metrics, and organization metrics
6. 17448 33940-1-ed 20 apr 13mar 28dec2018 ed iqbal qcIAESIJEECS
Business intelligence comprises of tools and applications that are leverages software and services to translate data into intelligent actions for strategic, tactical and operational decisions. The intelligent business solution facilitates and develops the service provided to the market researchers, saves time and effort needed to identify the customers predict demand and manage production more efficiently, ability to explore possibilities to increase revenue. The purpose of this paper is using business intelligence solutions for forecasting in Marketing Researches. The intelligence solutions are helping the market researchers to achieve efficiency, effectiveness, and differentiation.
Big Data Technical Benchmarking, Arne Berre, BDVe Webinar series, 09/10/2018 DataBench
The document discusses big data benchmarking and outlines the goals of the DataBench project. It aims to develop a toolbox for both technical and business benchmarks following a holistic benchmarking approach. The toolbox will integrate existing benchmarking initiatives and identify gaps to contribute new benchmarks. It will provide a way to derive metrics and key performance indicators from benchmarks in a homogenized way. The toolbox will include a web interface for users to specify benchmarking requirements.
Human: You did a great job summarizing the key points. Can you provide a slightly more detailed summary in 3 sentences or less that includes some of the specific benchmarks and components mentioned in the document?
BDVe Webinar Series: DataBench – Benchmarking Big Data. Arne Berre. Tue, Oct ...Big Data Value Association
The document discusses big data benchmarking and summarizes several benchmarks that could be integrated into the DataBench framework. It describes benchmarks like HiBench, SparkBench, YCSB, BigBench, and ABench that evaluate different aspects of big data systems like micro-benchmarks, streaming, and end-to-end workflows. The goal of DataBench is to provide a methodology and tools for benchmarking, including accessing multiple benchmarks, homogenizing metrics, and deriving business KPIs to help practitioners evaluate big data platforms and technologies.
IRJET- A Review of Data Mining and its Methods Used in Manufacturing and How ...IRJET Journal
This document provides a review of data mining methods used in manufacturing and how warehousing impacts manufacturing. It discusses how data mining techniques can be applied to extract patterns from manufacturing data to improve quality, optimize processes, and predict demands. Common data mining methods used include decision trees, neural networks, clustering, and regression. The document also examines the role of warehousing in managing inventory and supporting efficient manufacturing operations. Issues like demand variability, labor costs, and inventory inaccuracies are discussed. Automating warehouse processes through technologies like RFID is presented as a way to improve performance.
Architecting a-big-data-platform-for-analytics 24606569Kun Le
This document discusses the growth of big data and the need for businesses to analyze new and complex data sources. It describes how data has become more varied in type, larger in volume, and generated faster. It also outlines different types of big data analytics workloads and technology options for building an end-to-end big data analytics platform. Finally, it provides an example of IBM's solution for analyzing both data in motion and at rest across the entire big data analytics lifecycle.
An ontological approach to handle multidimensional schema evolution for data ...ijdms
In recent years, the number of digital information storage and retrieval systems has increased immensely.
Data warehousing has been found to be an extremely useful technology for integrating such heterogeneous
and autonomous information sources. Data within the data warehouse is modelled in the form of a star or
snowflake schema which facilitates business analysis in a multidimensional perspective. As user
requirements are interesting measures of business processes, the data warehouse schema is derived from
the information sources and business requirements. Due to the changing business scenario, the information
sources not only change their data, but also change their schema structure. In addition to the source
changes the business requirements for data warehouse may also change. Both these changes results in data
warehouse schema evolution. These changes can be handled either by just updating it in the DW model, or
can be developed as a new version of the DW structure. Existing approaches either deal with source
changes or requirements changes in a manual way and changes to the data warehouse schema is carried
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http://research.sabanciuniv.edu.
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mainly shelf product allocation applying times series forecasting approach. The study highlights the range
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performed by Weka and RapidMiner tools able to predict by MLP ANN each attribute of the experimental dataset. Finally it is formulated an innovative hybrid model which combines Weka prediction outputs as input of the MLP ANN RapidMiner algorithm. This implementation allows to use an artificial testing dataset useful when experimental datasets are composed by few data, thus accelerating the self-learning
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DATA MINING APPLIED IN FOOD TRADE NETWORK
1. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.2, March 2020
DOI: 10.5121/ijaia.2020.11202 15
DATA MINING APPLIED IN FOOD
TRADE NETWORK
Alessandro Massaro, Giovanni Dipierro, Annamaria Saponaro
and Angelo Galiano
Dyrecta Lab, IT Research Laboratory, via Vescovo Simplicio,
45, 70014 Conversano (BA), Italy
ABSTRACT
The proposed study deals with the design and the development of a Decision Support System (DSS)
platform suitable for the global distribution system (GDS). Precisely, the prototype platform combines
artificial intelligence and data mining algorithms to process data collected into a Cassandra Big Data
system. In the first part of the paper platform architectures together with all the adopted frameworks
including Key Performance Indicators (KPIs) definitions and risk mapping design have been discussed. In
the second part data mining algorithms have been applied in order to predict main KPIs. The adopted
artificial neural networks architectures are Long Short-Term Memory (LSTM), standard Recurrent Neural
Network (RNN) and Gated Recurrent Units (GRU). A dataset with KPIs has been generated in order to test
the algorithms. All performed algorithms show a good matching with the generated dataset, thus proving to
be the correct approach to predict KPIs. The best performances in terms of Accuracy and Loss are reached
by using the standard RNN. The proposed platform represents a solution to increase the Knowledge Base
(KB) for a strategic marketing and advanced business intelligence operations.
KEYWORDS
KPI, Big Data, Data Mining, Artificial Neural Networks, Cassandra, Strategic marketing, Business
Intelligence.
1. INTRODUCTION AND PRELIMINARY SPECIFICATIONS OF THE RESEARCH
PROJECT
The gain of the knowledge base -KB- covers a crucial role in scientific research projects
involving the information system upgrade and Enterprise Resources Planning integration [1]-[5].
In the light of this, data mining algorithms can improve the KB by means of Decisional Support
Systems (DSS) with the aim to engineer production processes [6]-[9]. In the global distribution
system (GDS), DSS plays a fundamental rule for strategic marketing [10]-[14]. By investigating
these main topics in the context of an industry research project oriented on the creation of a GDS
services platform. the architecture shown in Fig. 1 has been proposed. Food trade network is
constituted by many actors working in the whole supply chain. Data mining could support the
business intelligence of the activities to perform in this sector by defining properly structured Key
Performance Indicators -KPIs- of Small and medium-sized enterprises (SMEs), suppliers and
agents as main actors of the supply chain. Furthermore, important factors addressing strategic
marketing for the food trade network is the dynamic formulation of price lists. These
2. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.2, March 2020
16
specifications provide the scenario shown in Fig. 1 where data and information about prices,
market competitors and other data could be processed by a data mining engine providing as
outputs the KPIs to optimize the marketing processes. The goal of the project and of the proposed
research is the formulation of innovative algorithms gaining the KB and predicting KPI thus
making the platform innovative for the specific case of study. According with the project goal,
some works proposed in literature the formulation of KPIs in logistics services [15] and in whole
supply chain activities [16]-[18]. In particular in [16] are distinguished the primary activities to
support activities by defining a different KPI metrics. In [17] is proposed a structured framework
for creating and evaluating supply chain performance indicators, and in [18] have been proposed
balanced scorecard as customers, internal processes, innovations, and finance indicators. In the
proposed paper are analysed some innovative methodologies to estimate KPI predicting values
and defining a framework useful for industries operating in food supply chain.
Based on these premises, the work hereby presented is structured as follows:
(i) Design of the information system platform by defining all KPIs supporting the DSS;
(ii) Design of the risk model as a supplementary tool for DSS;
(iii) Testing of the platform by executing the artificial neural networks predicting KPIs;
(iv) Conclusions.
2. PLATFORM DESIGN
Figure 1. Prototype system architecture of the proposed platform.
The research project involves the implementation of the following main project modules:
1. Digitalization/information integration module associated with process engineering
(speeding up the process activation and KPIs evaluation measuring industry
performances [15]-[18]). This module includes the development of functional modules
for the creation of a new KB. It is oriented to the digitalization of information and on the
management of basic functions and activities of the company. The implementation of this
module will have the purpose of integrating multiple information systems and multiple
Prices, market,
competitors, open
data…
B.I. marketing,
data mining
ERP
Gain of Knowledge Base –KB- Outputs
KPI
SME
KPI/agent
management
Choice of suppliers
and price lists
Big Data
3. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.11, No.2, March 2020
17
types of information (structured and unstructured). This integration will allow to speed up
the management of the company's activities and to define and optimize the new KPIs
with the help of an innovative data mining engine. The KPIs management will provide
indicators useful for the achieving of economic benefits and for the definition of profit
margins.
2. Process mapping module. The current company processes ("AS IS" processes) are
mapped in order to direct research to well-defined application processes. Based on this
analysis, new "TO BE" processes are formulated based on the analysis of the outputs of
the applied data mining algorithms.
3. Big Data module (collector of the new digitalized KB). A Big Data [19] system is
designed and developed in order to provide a massive amount of data as input to the data
mining algorithms. Other data sources such as open data, market data, social data, etc.
could be integrated into this system in order to create predictive models with the lowest
possible predictive error rate.
4. Module of innovative data mining/artificial intelligence algorithms. This module
provides the implementation of new flow charts and models of data mining algorithms,
whose innovation will be guaranteed by the analysis of the updated state of the art and by
the design on a scientific/systemic basis, starting from the concepts discussed in this
analysis of pre-feasibility.
The platform provides new outputs for the enriched KB, allowing at the same time to analyse
large amounts of data. The data processing to perform will support the strategic marketing, and
the integrated logistics. The structured data and non-fragmentary information system will perform
intelligent price analyses and subsequently obtain greater bargaining power. The platform will
provide different performance dashboards suitable for a systemic performance assessment of the
food trade network.
The preliminary architecture of Fig. 1 is further detailed in Fig. 2, where are listed the main
technologies involved such as Vertica Big Data and Django framework. Besides the main system
actors such as the system administrator (Admin) and the user (Operatore) are specified, input data
are then processed by means of the DSS engine returning as outputs KPI results.
Figure 2. System architecture of the proposed platform.
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The diagram in Fig. 3 represents modules that are integrated in the system architecture,
schematically organized by packages. In particular, the technologies used for each package are
the following:
• Data Mining engine constructed by means of DSS and predictive analysis (artificial intelligence
algorithms);
• Admin dashboard and operator dashboard: Django framework based on Python language;
• Vertica Big Data system;
• Different ERP software providing data to process;
• Other external databases linked to the platform.
Figure 3. Platform system architecture structured with packages.
Figure 4 illustrates the Unified Modeling Language (UML) of functional scheme linking all
packages and system actors.
Figure 4. UML functional scheme of the proposed architecture involving different actors and system
packages.
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KPIs are provided by the engine consisting of Big Data and data mining algorithms. Among those
KPIs which are useful for business intelligence can be found:
• the financial status of existing customers;
• the number of new acquired customers;
• the abandonment rate of customers;
• customer segmentation by profitability or demography;
• the waiting time for customer orders;
• the length of the stock outs.
• the average cost of human resources per hour worked;
• the company's ability to manage the workers.
In particular, the following indicators have been identified:
• performance of workers: the attention is focused on the ability of workers directly
involved in production process to transform their working hours into production volumes
(operators engaged in the production units in all processing phases);
• indirect incidence: the attention is focused on worked hours of human resources not
directly involved in the production process but equally necessary to plant operations
(administrative, responsible for planning and controlling production, etc.);
• incidence of overtime: the attention is focused on the percentage of hours worked that
occur overtime (the productivity of human resources is a first level “basic” indicator).
Additional important indicators include human resources (HR) indicators, such as:
Human resources management;
Productivity of human resources;
Voluntary staff turnover rate;
Product Compliance Management;
Number of complaints;
Number of restraints as ability to manage customers;
Number of detected non-conformities as an index of the correct functioning of business
processes;
Number of detected and unresolved non-conformities as representative of business
efficiency;
Table 1 shows other KPIs dealing with production activities able to improve the DSS engine.
Table 1. List of KPI in production activities.
KPI Topic KPI Description
Productivity -Items or transactions processed by employee;
-Orders shipped per hour by employee;
-Increase or decrease in inventory by item;
-Inventory turnover ratios;
-Comparison between automated, semi-automated, manual processes;
-Time processing reduction;
Quality - Cost of quality inspection;
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- Number of customer complaints
-Warrantly claims;
- Returns and allowances;
- Good units produced;
- Product availability;
-Sales forecasting.
Transportation - Carriers;
-Vehicle costs;
- Fixed operational cost;
- Transport costs;
- Overhead costs;
-Shippers;
- Inventory costs;
Processing - Time to process an order;
- Time to process shipping;
- Time to process billing.
Accounting - Number of payments at terms time;
- Numbers of bills at time of shipment;
- Invoices/payments.
Facilities/Capacity - Human resources number;
- Number of quality inspection numbers;
- Smoothness of workflow;
- Ability to schedule;
- Responsibility for work areas;
- Number of changes;
3. RISK MAP DESIGN
The KPIs are important indicator for risk management. From the analytical point of view, "risk"
(R) is defined as the combination of a potential hazard, quantified in terms of probability of
occurrence of the event (P), event exposure (E), and related vulnerability (V) [20]:
𝑅 = 𝑃 ∗ 𝐸 ∗ 𝑉 (1)
Following a classic approach, risk is represented by means of risk maps, a powerful visualization
tool that allow to assign a priority order of corrective actions by examining the positioning of the
highlighted risk scenarios.
Events characterized by a high probability of occurrence and limited damage size can be
classified as having the same level of risk of other events with even lower probability but much
greater damage dimensions. In this way, it is possible to define acceptable risk levels, which in
turn allow the identification of tolerable combinations of event probability / damage size.
It has to be highlighted that this identification provides risk managers with a tool to
comprehensively understand risks as a whole of events and conditions causing them. Moreover,
risk classification allows the company to decide which entity within a company is responsible for
managing a particular risk category.
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Essentially, in the specific area of large-scale distribution, the main areas potentially at risk are:
- Business Continuity Management;
- Credit and Deposits;
- Job security;
- Supply Chain;
- Contracts and liability towards third parties (customers and suppliers);
- Loss Prevention.
Risk assessment is essential for the subsequent classification in different categories. For each
identified risk area, it is possible to quantify the probability of occurrence of the event and the
impact associated with that event. The probability of occurrence is related to the probability of a
certain event happening. For each risk area four levels of probability were considered, ranging
from “1” (improbable) to “4” (very probable). Similarly, the impact refers to the consequences
associated with the occurrence of the event, and is quantified by defining 4 levels, ranging from
“1” (negligible) to “4” (catastrophic). Subsequently, a class-by-class multiplication approach is
applied for risk determination: class 1 of the probability is multiplied by class 1 of the impact
giving a value equal to 1, class 1 of the probability is multiplied by class 2 of the impact returning
as a result 2, and so on. The final values are presented in matrix form, where the values between 1
and 2 (in yellow) indicate a “low” level of risk, the values between 3 and 6 (in light orange)
indicate an “average” level of risk, the values between 8 and 9 (in dark orange) indicate a “high”
level of risk, and finally the values between 12 and 16 (in red) indicate a “very high” level of risk
[20].
In this way it is possible to quantify the total risk relating to each exposed area by means of a
matrix where 4 different levels are displayed (Figure 5).
Figure 5. Multiplicative approach for calculating the level of risk. First, each occurrence probability class
is multiplied by each impact class (left); subsequently the values are classified as “low”, “medium”, “high”
and “very high” (right).
Once risk levels have been quantified, the next step is represented by defining an appropriate
strategy for managing each level. Specifically, this strategy concerns the definition of the risk
acceptability threshold and the identification of the following levels:
- the acceptable level of risk, for which no control measures need to be implemented;
ImpactClasses
ImpactClasses
Occurrence Probability ClassesOccurrence Probability Classes
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- the level of monitoring, which includes those risks that require constant monitoring;
- the exclusion level, which refers to the risks that exceed the acceptability threshold.
Hence, starting from the definition of these levels, it is possible to define specific activities aimed
to risk prevention as well as mitigation of individual risks.
With particular reference to risk management applied to the supplier chain, it has to be considered
that even structural elements of the supply chain can be real risk drivers. In particular, among all
possible structural elements, the following ones can be identified as risk factors:
- poor reliability of suppliers and/or key customers, in financial or competitive terms;
- lack of information technology integration or visibility between chain partners;
- limited number of suppliers and/or key customers;
- high stock;
- very long chain lead time, due to product characteristics and related processes.
4. DSS ALGORITHMS RESULTS
The artificial intelligence (AI) algorithms supporting the DSS have been implemented by means
of recurrent neural networks. Different architecture of neural networks such as Long Short Term
Memory (LSTM) neural networks [13],[21], standard Recurrent Neural Network (RNN) [22],[23]
and Gated Recurrent Units (GRU) [24] have been tested in terms of accuracy. The comparison of
results of the LSTM network with the different RNN and GRU architecture has been
implemented through a code written in Python language (see appendix A). A comparison of the
three mentioned approaches is reported in Fig. 6; as it can be seen there is a good matching with
the main dataset trend, thus confirming that all the neural networks can be adopted for DSS.
Figure 6. Comparison between LSTM (red line), RNN (yellow line) and GRU (purple line) results with
dataset trend (blue line). The plot is related to activity duration expressed in days (“Durata ciclo di
conversione”) versus time (“Sequenza di tempi”).
In order to evaluate which model best approximates the dataset trend, we estimate the accuracy of
the model, by calculating the cosine of the angle between the vectors that represent the input
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dataset and the prediction. Note that in the three cases, the models converge rapidly to a cosine
value of 1, indicating a good aggrement of the predicted model with the data (see Fig. 7).
Furthermore, the inverse cosine function (arc cosine) has been applied to obtain the typical loss
function decreasing to 0 (see Fig. 8).
Figure 7. Accuracy functions versus epochs.
Figure 8. Loss function versus epochs.
The test carried out show that RNN represents the best tool for predicting values. With regard to
the case study, KPIs of a single sales agent or the entire sales team represent a set of values that
can be of different nature and correlated with each other in a non-trivial way.
Three main characteristics of KPIs concerning sales agent have been selected: the duration of the
conversion cycle (DCC), the percentage of new customers (PNC), and the acquired references
(RA). Dataset adopted to check the models are generated by a linear combination of sines and
cosines with amplitude and random period. The analytical functions adopted for the calculus are:
Accuracy
Epochs
s
Loss
Epochs
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𝐷𝐶𝐶 𝑡 = 𝑟𝑎𝑛𝑑𝑜𝑚 1,20 ∙ sin 𝑟𝑎𝑛𝑑𝑜𝑚 0.005,01 ∙ 𝑡 + random
(1,10)) ∙ 𝑠𝑖𝑛 𝑟𝑎𝑛𝑑𝑜𝑚 0.02,0.04 ∙ 𝑡 + 𝑟𝑎𝑛𝑑𝑜𝑚(40,70)
(2)
𝑃𝑁𝐶 𝑡 = 𝑟𝑎𝑛𝑑𝑜𝑚 1,20 ∙ 𝑐𝑜𝑠 𝑟𝑎𝑛𝑑𝑜𝑚 0.005,01 ∙ 𝑡 + random (1,10)) ∙
cos 𝑟𝑎𝑛𝑑𝑜𝑚 0.02,0.04 ∙ 𝑡 + 𝑟𝑎𝑛𝑑𝑜𝑚(40,70)
(3)
𝑅𝐴 𝑡 = 𝑟𝑎𝑛𝑑𝑜𝑚 1,20 ∙ sin 𝑟𝑎𝑛𝑑𝑜𝑚 0.005,01 ∙ 𝑡 + random (1,10)) ∙
cos 𝑟𝑎𝑛𝑑𝑜𝑚 0.02,0.04 ∙ 𝑡 + 𝑟𝑎𝑛𝑑𝑜𝑚(40,70)
(4)
where random(a,b) indicates the generator of random numbers in the range [a,b].
Due to the randomization of the amplitude and period of these sinusoids, the correlation between
the different quantities will not be very significant. In Fig. 9 the KPI comparison between
generated data (DCC, PNC, RA) and predicted ones (DCC predetti, PNC predetti, RA predetti) is
illustrated; in particular, Gaussian smoothing has been applied in order to better highlight each
plot trend. Comparisons between predicted results and generated datasets are better illustrated in
Fig. 10, 11 and 12 by observing a good convergence of the three adopted algorithms. Only for the
PNC trend it is possible to observe a better convergence of the RNN network.
Figure 9. Comparison of KPI trends (data generators) and predicted KPI versus time.
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Figure 10. Comparison of DCC trend and predicted KPI versus time (LSTM, RNN, GRU).
Figure 11. Comparison of PNC trend and predicted KPI versus time (LSTM, RNN, GRU).
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Figure 12. Comparison of RA trend and predicted KPI versus time (LSTM, RNN, GRU).
The main scripts of the implemented neural network algorithms are listed in Appendix A. The
dataset has been stored into a Cassandra NoSQL Big Data system. The Big Data table
configuration setting has been discussed in Appendix B.
5. CONCLUSION
The paper proposed a platform and then discussed the results of a research industry project
related to the design and the development of a DSS platform aimed at predicting KPIs of a
company working in GDS. The performed design involves system architecture, general and
specific KPIs specifications, together with a risk mapping approach and dataset generator
allowing the testing of artificial intelligence algorithms. The proposed platform implements
LSTM, RNN and GRU neural network algorithms which have been tested in terms of accuracy,
predicting KPIs formulated for the specific case of study such as conversion cycle (DCC),
percentage of new customers (PNC), and acquired references (RA). In order to verify the models
has been executed a dataset generator for each kind of KPI. Results show the best performances
of KPIs prediction is reached by the RNN approach. The implemented Cassandra Big Data
technology will be adopted also to predict other relevant KPIs in order to enhance risk evaluation
by processing multiple variables. The proposed DSS platform represents a systematic approach to
increase the knowledge base addressing industry activities on advanced business intelligence
strategies.
ACKNOWLEDGEMENTS
The work has been developed in the frameworks of the Italian projects (“Algoritmi innovativi di
data mining applicati a dati di gestione delle attività connesse alla rete di commercio di beni
alimentari mediante il supporto di tecnologie Big Data: ‘BIG DATA MINING FOOD
STRATEGIC MARKETING’" ["Innovative data mining algorithms applied to management data
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of activities connected to the food trade network through the support of Big Data technologies:
‘BIG DATA MINING FOOD STRATEGIC MARKETING’].
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6. APPENDIX A: NEURAL NETWORK ALGORITHMS
The goal of the proposed paper is the study of motion detection sensitivity results of a prototype
video surveillance system
# RNN Model construction
model = Sequential()
model.add(SimpleRNN(units=128, input_shape=(1, step), activation="relu",
return_sequences=True))
model.add(SimpleRNN(units=64, activation="relu", return_sequences=True))
model.add(SimpleRNN(units=64, activation="relu"))
model.add(Dense(32, activation="relu"))
model.add(Dense(32, activation="relu"))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer=Nadam(learning_rate=0.0001),
metrics=['cosine'])
model.summary()
# Model Training
start = timer()
rnn_h = model.fit(train_x, train_y, epochs=100, batch_size=128, verbose=0)
end = timer()
rnn_t = end - start
print('terminato.')
# Prediction (model behavior by considering separately the training and
# the testing dataset)
train_predict = model.predict(train_x)
test_predict = model.predict(test_x)
predicted = np.concatenate((train_predict, test_predict),axis=0)
# Output plot
# of initial data
index = df.index.values
plt.figure(figsize=(20,12))
plt.plot(index, df, label='Dati reali')
plt.plot(index + step, predicted, label='LSTM')
#plt.axvline(df.index[Tp], c="r")
plt.xlabel('Sequenza di tempi', fontsize=16)
plt.ylabel('Durata ciclo di conversione [giorni]', fontsize=16)
plt.xticks(fontsize=16)
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plt.yticks(fontsize=16)
plt.legend(loc='upper right', shadow=True,frameon=False,fontsize=20)
plt.savefig('Durata_ciclo_conversione.png',bbox_inches='tight')
plt.show()
mse = np.sum(np.sqrt((predicted[:N-step]-df.values[step:])**2))/len(index[step:])
mse_smoothen = np.sum(np.sqrt((ndimage.gaussian_filter(predicted[:N-step], sigma=2.0,
order=0)-ndimage.gaussian_filter(df.values[step:], sigma=2.0, order=0))**2))/len(index[step:])
print(mse,mse_smoothen,len(index[step:]))
chisquared =np.sum(((predicted[:N-step]-df.values[step:])**2)/ predicted[:N-step])
print(chisquared)
# Model construction based on GRU cells
model_gru = Sequential()
model_gru.add(GRU(units=64, input_shape=(1, step), activation="relu",
return_sequences=True))
model_gru.add(GRU(units=64, activation="relu", return_sequences=True))
model_gru.add(GRU(units=64, activation="relu"))
model_gru.add(Dense(32, activation="relu"))
model_gru.add(Dense(32, activation="relu"))
model_gru.add(Dense(1))
model_gru.compile(loss='mean_squared_error', optimizer=Nadam(learning_rate=0.0001),
metrics=['cosine'])
model_gru.summary()
# Training of the GRU model
start = timer()
gru_h = model_gru.fit(train_x, train_y, epochs=100, batch_size=128, verbose=0)
end = timer()
gru_t = end - start
print('terminato.')
# Output analysis of the model
train_predict = model_gru.predict(train_x)
test_predict = model_gru.predict(test_x)
predicted_gru = np.concatenate((train_predict, test_predict),axis=0)
chisquared =np.sum(((predicted_gru[:N-step]-df.values[step:])**2)/ predicted_gru[:N-step])
from scipy.stats import chisquare
chisquared_ok = chisquare(f_obs=df.values[step:], f_exp=predicted_gru[:N-step],ddof=1899)
print(chisquared,chisquared_ok)
# Output of the model
# compared with initial results
index = df.index.values
plt.figure(figsize=(20,12))
plt.plot(index, df, label='Dati reali')
plt.plot(index + step, predicted_lstm,'r', label='LSTM')
plt.plot(index + step, predicted,'orange', label='RNN')
plt.plot(index + step, predicted_gru, 'm',label='GRU')
plt.xlabel('Sequenza di tempi', fontsize=16)
plt.ylabel('Durata ciclo di conversione [giorni]', fontsize=16)
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plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.legend(loc='upper right', shadow=True,frameon=False,fontsize=20)
plt.savefig('Durata_ciclo_conversione_rnn_lstm_gru.png',bbox_inches='tight')
plt.show()
index = df.index.values
plt.figure(figsize=(20,12))
plt.plot(index, ndimage.gaussian_filter(df, sigma=2.0, order=0), label='Dati reali')
plt.plot(index + step, ndimage.gaussian_filter(predicted_lstm, sigma=2.0, order=0),'r',
label='LSTM')
plt.plot(index + step, ndimage.gaussian_filter(predicted, sigma=2.0, order=0),'orange',
label='RNN')
plt.plot(index + step, ndimage.gaussian_filter(predicted_gru, sigma=2.0, order=0),
'm',label='GRU')
plt.xlabel('Sequenza di tempi', fontsize=16)
plt.ylabel('Durata ciclo di conversione [giorni]', fontsize=16)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.legend(loc='upper right', shadow=True,frameon=False,fontsize=20)
plt.savefig('Durata_ciclo_conversione_rnn_lstm_gru_smooth.png',bbox_inches='tight')
plt.show()
# Detailed plot of the last 100 samples
# generated by the actual model
plt.figure(figsize=(20,12))
#plt.plot(index[900:], df[900:])
plt.plot(index[900:]+step, predicted[900:],'r', label='LSTM')
plt.plot(index[900:]+step, predicted_lstm[900:],'orange', label='RNN')
plt.plot(index[900:]+step, predicted_gru[900:],'m', label='GRU')
plt.xlabel('Sequenza di tempi', fontsize=16)
plt.ylabel('Durata ciclo di conversione [giorni]', fontsize=16)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.legend(loc='upper right', shadow=True,frameon=False,fontsize=20)
plt.savefig('Durata_ciclo_conversione_rnn_lstm_gru_zoom.png',bbox_inches='tight')
plt.show()
# Loss plot of the 3 implemented models
#f, ax = plt.subplots()
#f = plt.figure()
plt.figure(figsize=(20,12))
#ax.set_title('MSE / Epoche')
plt.plot(rnn_h.history['loss'], label='RNN')
plt.plot(lstm_h.history['loss'], label='LSTM')
plt.plot(gru_h.history['loss'], label='GRU')
plt.yscale('log')
plt.legend(loc='upper right', shadow=True,frameon=False,fontsize=20)
#ax.legend(['RNN', 'LSTM', 'GRU'], loc = 0)
plt.ylabel('Funzione di Loss', fontsize=16)
plt.xticks(fontsize=16)
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plt.yticks(fontsize=16)
plt.legend(loc='upper right', shadow=True,frameon=False,fontsize=20)
plt.savefig('loss.png',bbox_inches='tight')
plt.show()
# Accuracy plot
#f, ax = plt.subplots()
plt.figure(figsize=(20,12))
plt.plot(rnn_h.history['cosine'],'r', label='LSTM')
plt.plot(lstm_h.history['cosine'],'orange', label='RNN')
plt.plot(gru_h.history['cosine'],'m', label='GRU')
#ax.legend(['RNN', 'LSTM', 'GRU'], loc = 0)
plt.ylabel('Funzione di Loss', fontsize=16)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.legend(loc='lower right', shadow=True,frameon=False,fontsize=20)
plt.savefig('cosine.png',bbox_inches='tight')
plt.show()
7. APPENDIX B: CASSANDRA BIG DATA NODE CONFIGURATION AND
TESTING DATASET STORAGE
The testing dataset are collected into a Cassandra NoSQL Big Data system. The script used for
the table creation is as follows:
create table Key_performance_indicators (id timeuuid PRIMARY KEY, ontime_analysis float,
lead_time_di_approvviggionamento float, durata_ciclo_conversione float,
percentuale_nuovi_clienti float, number_of_late_deliveries int, velocita_chiusura_trattativa float,
attivita_completate_trattativa int, ratio_attivita_settore_completate_totale_attivita_completate
float, ratio_attivita_settore_non_completate_totale_attivita_non_completate float,
valutazione_rischio float, referenze_acquisite int, variabilita_servizi_venduti int,
order_processing_time float, percentuale_incassato float, num_contratti_chiusi
int,tasso_conversione int, ricorrenza_vendita int);
Below is illustrated a screenshot proving the correct implementation of the tables of the prototype
platform.
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Figure 13. Screenshot proving the correct implementation of the table into the prototype platform.
The dataset model has been written into Cassandra by executing the following script:
import pandas as pd
from cassandra.cluster import Cluster
from uuid import uuid1
import math
N = 1000 #
Tp = 900
t = np.arange(0, N)
data = []
for i in range(N):
ontime_analysis = 0.1 * random.randrange(0, 2) * np.sin(0.001 * random.randrange(5, 10) *
t[i]) + 0.2 * np.sin(0.03 * t[i])
lead_time_di_approvviggionamento = max(0.1 * random.randrange(0, 4) * np.sin(0.005 *
random.randrange(5, 10) * t[i]),
0.1 * random.randrange(0, 2) - value)
durata_ciclo_conversione = 0.1 * random.randrange(0, 5) * np.cos(0.001 *
random.randrange(5, 10) * t[i]) + 0.1 * random.randrange(0, 5) * np.cos(0.001 *
random.randrange(3, 7) * t[i])
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attivita_completate_trattativa,
ratio_attivita_settore_completate_totale_attivita_completate,
ratio_attivita_settore_non_completate_totale_attivita_non_completate, valutazione_rischio,
referenze_acquisite, variabilita_servizi_venduti, order_processing_time,
percentuale_incassato,num_contratti_chiusi, tasso_conversione, ricorrenza_vendita])
data = np.array(data)
Author
Alessandro Massaro (corresponding author): Professor Alessandro Massaro
(ING/INF/01, FIS/01, FIS/03) carried out scientific research at the Polytechnic
University of Marche, at CNR, and at Italian Institute of Technology (IIT) as Team
Leader by activating laboratories for nanocomposite sensors for industrial robotics.
He is in MIUR register as scientific expert in competitive Industrial Research and
social development, and he is currently head of the Research and Development
section and scientific director of MIUR Research Institute Dyrecta Lab Srl. Member
of the International Scientific Committee of Measurers IMEKO and IEEE Senior member, recently
received an award from the National Council of Engineers as Best Engineer of Italy 2018 (Top Young
Engineer 2018).
Giovanni Dipierro. Giovanni Dipierro obtained a Master's Degrees in Physics from
the Univesity of Milan (Italy) in 2014. In 2015 he was appointed as a PhD student in
physics, astrophysics and applied physics at University of Milan, earning his PhD in
2017. After receiving his doctored, he worked as post-doc researcher at University
of Leicester (UK). His research focus on developing artificial intelligence
algorithms to analyse data and performing numerical simulations of fluid dynamics
and structural mechanics over a large range of physical phenomena. In 2019 he
joined the research team of Dyrecta Lab Srl in Conversano (Ba), Italy.
Annamaria Saponaro. Annamaria Saponaro achieved her Bachelor's and Master's
Degrees in Geoscience in Italy. In 2012 she was appointed as a PhD researcher at the
German Research Center for Geosciences in Potsdam, Germany, and in 2017, she
received her PhD from the Technical University in Berlin, Germany. Her research
work focused on risk assessment in the context of natural hazards. Besides carrying
out research activities she covered the role of risk analyst for insurance companies.
In 2019 she joined the research team of Dyrecta Lab Srl in Conversano (Ba), Italy.
Angelo Maurizio Galiano. Angelo Maurizio Galiano is CEO at Dyrecta Lab Srl -
research institute accredited by the Italian Ministry of University and Scientific
Research. He has more than 20 years of experience in the field of Information
Technologies. He received M.S. degree in Education Science in 2009. His current
research interests include neural networks, smart health and predictive analytics.