Broadband Subscriber Data Integration with ETL to SaaS

A variety of studies have focused on the effect of competition in broadband markets on increasing broadband penetration rates. Few studies however have focused on the extent to which competition also results in innovation in the marketplace, as demonstrated by increased broadband speeds, and other improvements that provide value to broadband users. This paper considers the effectiveness of market competition as a means of encouraging broadband providers to offer innovative services that meet citizens’ increasing needs for affordable high quality and high speed broadband connectivity. The study focuses on the provision of broadband services in Canada, a country where consumers were early adopters of broadband and where the policy environment has encouraged competition in the broadband market from its inception. Drawing on data from the OECD and the Canadian Radio-television and Telecommunications Commission (CRTC), the paper demonstrates that Canada’s regulatory regime does not appear to be sufficient to enable a competitive marketplace that results in the provision of innovative broadband services.

The IFC File Analyzer software generates a spreadsheet or a set of CSV (comma-separated value) files from an IFC file. IFC (Industry Foundation Classes) is the data exchange standard used to facilitate interoperability in the building and construction industry. IFC is developed by buildingSMART and is an ISO standard—ISO 16739. Typical IFC viewers show avisualization of the building represented by the IFC file. The user can drill down to the individual attributes for a single building object. However, there is no way to view all of the entities and their attributes at once. The IFC File Analyzer provides this capability by creating a spreadsheet or CSV files from the IFC file. In the spreadsheet, a worksheet is created for each type of IFC entity in the file. Every row in the worksheet contains the attributes for an instance of an IFC entity. Multiple IFC files can be analyzed at once to compare entity usage. There are options to select which types of IFC entities are processed and to report some of the IFC entity Inverse relationships.

The project follows the development of a Java Software Tool that extracts data from Flat File (Fixed Length Record Type), CSV (Comma Separated Values), and XLS (Microsoft Excel 97-2003 Worksheet file), apply transformation to those sources, and finally load the data into the end target RDBMS. The software refers to a process known as ETL (Extract Transform and Load). Those kinds of systems are called ETL systems. The analysis involved research on the theory behind the ETL process as well as the theory behind the various phases of the applied methodology. Also an in-depth look at the design and architecture of the software has been made. To create a complete design needed to be used for the implementation, different techniques and diagrams where used to visualise and refine ideas: UML class diagrams, System Architecture Diagrams, Physical Data Model, and Project Timeline. The implementation of the project involved the translation of the system architecture into working software using the Extreme Programming Methodology and the Java programming language. A mapping algorithm module and design patterns have been used in the implementation phase. A transformation syntax has been defined to achieve data transformation. The testing of the software was done in the form of a unit test. A formal test plan was prepared to ensure that the main features of the system worked as defined. An error handling code implementation has been developed to avoid an unexpected crash of the system and to communicate to the user problems or errors.

Objectives: To retrieve tweets from Twitter through Twitter API. The domain chosen for analysis is Make-In-India Dataset. Methods/Statistical Analysis: This paper consists of two phases of work: 1. Data Streaming from Twitter, 2. Knowledge Mining through R-Studio. Methods used for the two key operations are: 1. Twitter API and 2. Sentiment Analysis through R. Twitter application is created to request for connection with the Twitter database. Once connection establishes, ­authentication keys are generated. Providing the search key “Make-In-India” and number of keys required, a file with .df (data frame) is generated with the tweets and is converted into .CSV (Comma Separated Values) file which is suitable Analysis. Sentiment Analysis1 is also called Opinion mining talks about retrieving facts from the tweets such as how many people supporting Make-In-India (or) how many are negative with the scheme (or) how many are neutral with it. For this process, a negative words file and a positive words file is taken for comparison with the tweet data to calculate positive score and negative score of the tweet. The difference of these scores gives us with the final score of the tweet. Findings: The number of tweets identified as positive (or) negative (or) neutral so that the status of Make-In-India can be ­visualised in a graph. Firstly, the extraction of Tweets is from Twitter through R-Studio Environment About “Make-In-India”. Secondly, we parse the extracted raw tweets using R according to the types and store in .CSV format in R database. Scores are calculated for allthe tweets and stored in a file. In the third, we perform visual analysis from the stored data using R statistical software to conclude the impact of the program. Application/Improvements: Application of the ­methodology is to get findings2 from the public opinions which are available from Twitter tweets on a particular government issue, political parties and medical status around the country. Also it is useful to assess the popularity of the political leader and the program. Decision making is possible through sentiment analysis of user tweets. Keywords: Big Data, Data Analytics, Make-In-India Data Set, Streaming, R-Studio, Tweets, Twitter API

Web Services have become an important means of integration in an organisation and in business to business practices. Of particular interest is the large volumes of structured information that can result from a backup of data in a system, as part of a search requested on the stored data or from some other data exchange requirement. To be a viable in these areas the simple object acceptable proctorial (SOAP) message used must be able to move the data in a secure and fault tolerant manner, but be able to do it in a fast and efficient manner. One of the largest problems in efficiency of using SOAP messages is in the large amount of information describing the structure and content of the message. This paper presents an examination of the size of SOAP document’s used in data exchanges when compressed, which also compares the text based document, a comma separated value (CSV) file, containing the same information. Our results suggest significant size cost savings can be made when the SOAP message is compressed. Therefore, the SOAP message contains sufficient data entries this size cost saving makes to total size of the message comparable to the compressed CSV file.

A large percentage of the data published on the web is tabular data, commonly in the comma-separated values (CSV) format. URI fragment identifiers for CSV provide possibilities for data publishers (web servers) to refer to specific parts (i.e., fragments) of a CSV file using URIs as well as for data consumers (web clients) to retrieve URI-identified fragments over HTTP for further processing. However, few studies have focused on CSV fragment retrieval, and to our knowledge, so far there are no implementations of CSV fragment identifiers compliant web clients/servers. This study addresses the issue of implementation strategies for CSV fragment retrieval over the HTTP protocol. Two strategies (i.e., client-side fragment evaluation and server-side fragment evaluation) including the fragment evaluation algorithms were proposed and then implemented in order to demonstrate the implementability of CSV fragment retrieval over HTTP and to experimentally observe and compare the run-time efficiency of the two strategies. The results indicate that server-side fragment evaluation is a more efficient strategy in terms of running time and bandwidth cost, but requires extensions for existing web servers, whereas client-side fragment evaluation is the opposite. Our methods and findings provide valuable reference for potential implementers.

The KV-Sentinel, established in 2004, is a joint project of the Robert Koch Institute and the 17 associations of statutory health insurance physicians (ASHIPs) in Germany. The ASHIPs provide anonymous physicians billing data to the Robert Koch Institute. The aim of this article is to describe methodological approaches for processing these routine data to determine vaccination coverage and incidence of vaccine preventable diseases. Furthermore, we discuss limitations in interpreting these data. The ASHIPs perform a data query of all vaccinations and of ICD-10 codes for pertussis, measles, mumps, varicella and herpes zoster and send anonymous data to the Robert Koch Institute. We perform routine tests to ensure data quality. Study population is the statutory health insured population (85.5% of the German population). Vaccination coverage is determined by the number of vaccinated persons and the number of statutory health insured persons. Incidence is calculated by the number of diseased persons per 100 000 statutory health insured persons. All 17 ASHIPs participate in the project. In total, 95 905 605 data records for vaccinations and 4 570 919 data records for pertussis, measles, mumps, varicella and herpes zoster were provided from 2004 to 2007. After performing routine tests with regard to structure and content of data, more than 99% of the data records can be analysed. In 2007, the majority of given vaccinations were monovalent vaccinations against influenza (39%) and tick-borne encephalitis (17%). In 2006 and 2007, 1 893 790 data records for diagnoses were provided. Of these, 75% were acute diagnoses and of these 70% were confirmed diagnoses. Most often, ICD-10 codes for herpes zoster (57%) and varicella (35%) were reported. Nationwide vaccination coverage of statutory health insured persons by age group can be determined by using billing data. It is possible to validate billing data of vaccinations with available data from other studies. Interpretation of billing data of acute vaccine preventable diseases remains challenging because it is difficult to assess potential under- or overestimation without the possibility of external validation. Therefore, further research is needed.

The debate of net neutrality and the potential regulation of net neutrality may fundamentally change the dynamics of data consumption and transmission through the Internet. The existing literature on economics of net neutrality focuses only on the supply side of the market, that is, a broadband service provider (BSP) may charge content providers for priority delivery of their content to consumers. In this article, we explore a complete spectrum of broadband network management options based on both the supply and demand sides of the market. We find that although the BSP always prefers the non‐neutral network management options, it does not always discriminate both sides of the market. From the social planner's perspective, we find that some network management options maximize the social welfare under certain market conditions while other options reduce the social welfare. Using the terminology from a recent Federal Communications Commission report and order, we categorize the social welfare maximizing options as “reasonable network management” and the social welfare reducing options as “unreasonable discrimination.” We also identify conditions under which the BSP's network management choices deviate from the social optimum. These conditions help establish the criteria under which the social planner might wish to regulate the BSP's actions.

Efficiency in broadband service provision: A spatial analysis

Insurance is a contract whereby an individual obtains financial protection against losses from an insurance company against the risks of financial losses as mentioned in the insurance. Many types of insurance exist today and there are so many companies that offer insurance services. These companies always need to predict whether or not a person will buy insurance so that they can save time and money for the most profitable customers. The task of insurance prediction in which the travel insurance prediction can be taken into progress using the machine learning using python. We here get the data from the databases from the companies. Here our task is to train a machine learning model to predict whether an individual can purchase the insurance policy from the company or not. We also use CSV (comma separated value) files in the process.

The Internet of Things (IoT) is rapidly reshaping smart homes with innovative technologies for everyday convenience. Although smart home technologies are becoming more popular, many homeowners lack the expertise and skills to operate them. Particularly mismanaged networking technologies often result in homeowners complaining to their Broadband Service Providers (BSPs) when there are disruptions to the performance of the smart home devices and services. Current remote management solutions used by BSPs offer limited functionality and accuracy in identifying the causes of these disruptions. Causes may include legitimate factors, such as misconfiguration, interference, and congestion, but they could also be the result of cyber attacks. This review exposes knowledge gaps that impede the development of effective troubleshooting tools (particularly related to cyber attacks) for BSPs, including network-related challenges and dataset limitations. We also discuss future research directions, emphasising scalable and data-efficient methods to reduce storage requirements, costs, and privacy concerns, which will enable BSPs to manage millions of customer networks more effectively.

What are the Bounds of the FCC's Authority Over Broadband Service Providers? A Review of the Recent Case Law

The residential internet services are operated via Residential Gateway (RG) routers otherwise known as Customer Premises Equipment (CPE). The RGs are either supplied by the Broadband Service Provider (BSP) or are bought directly from the marketplace. These RG routers have a general-purpose uplink connection to the internet through their BSP’s network. There isn’t a default encryption or firewall involved between the residential Local Area Network (LAN) and the mighty internet. All data on the phones, computers, laptops and Internet of Things (IoT) devices is exposed to the internet via the RG routers. Certain BSPs offer security features for an additional fee; nevertheless, subscribers may remain susceptible to threats such as phishing, web advertisements, port scans, and unauthorized data collection. Given the depth of internet and the unwanted imposition it brings to a local network, it is important that home networks are provisioned with enterprise-grade security, privacy and storage capabilities. There is a way to implement a residential end to end, reproducible approach for building a home network stack— including a perimeter firewall/router, local DNS with ad blocking and encrypted upstream resolution, and a network attached storage (NAS)—using a general-purpose computer or laptop, and open-source software.

In the rapidly evolving digital landscape, businesses are increasingly dependent on data to drive strategic decisions and maintain a competitive edge. The integration of data engineering techniques with Business Intelligence (BI) has become a key enabler for organizations to transform raw data into actionable insights. This paper explores how modern data engineering practices, such as Extract, Transform, Load (ETL) pipelines, data lakes, and data warehousing, play a pivotal role in enhancing BI systems. These practices streamline the process of data extraction from diverse sources, data transformation for consistency, and storage in optimized repositories to facilitate analysis. Moreover, the implementation of real-time data processing and cloud-based solutions has revolutionized how businesses manage large-scale datasets, enabling the handling of both structured and unstructured data with increased speed and flexibility. The rise of big data technologies such as Hadoop, Spark, and NoSQL databases further extends the ability to perform complex queries and predictive analytics, empowering decision-makers with timely, accurate insights. This abstract underscores the importance of building scalable, efficient data infrastructures through advanced data engineering methodologies. By integrating these techniques, businesses can ensure data quality, improve operational efficiency, and support comprehensive BI systems that drive innovation. Ultimately, this leads to more informed decision-making, enhanced customer experiences, and greater adaptability in the face of market changes. The effective combination of data engineering and BI is critical for leveraging data as a strategic asset in today’s competitive business environment.

Software testing is essential in the software development process. Modified Condition / Decision Coverage (MC/DC) is a test case derivation technique that enhances the stability and reliability of software by effectively verifying complex conditions and decision structures. We propose the MCC Test Cases (MTC) generator in this study. This generator generates Multiple Condition Coverage (MCC) test cases using binary numbers to confirm the maximum coverage value of MC/DC testing. The proposed MTC generator utilizes some conditions from the Traffic Alert and Collision Avoidance System (TCAS)-II specification. It converts them into a Comma-Separated Values (CSV) file and then validates the coverage results through the VectorCAST program. So, MC/DC testing was performed using the MCC test case to confirm the maximum coverage value when performing MC/DC tests for each condition of the TCAS-II specification. This research is helpful for the verification of MC/DC test cases by confirming the maximum coverage value when performing MC/DC tests. Moreover, having more test cases increases the likelihood of discovering defects. Therefore, it can improve the efficiency of software test coverage verification, as well as the quality and stability of software.