Problem Statement

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A financial services firm faced inefficiencies in generating accurate and timely financial reports. The manual reporting process was labor-intensive, prone to errors, and delayed decision-making. With increasing data complexity and regulatory requirements, the firm sought an automated solution to streamline financial reporting while maintaining high accuracy.

Challenge

Implementing an automated financial reporting system involved addressing the following challenges:

Aggregating and consolidating large volumes of financial data from disparate sources in real time.

Ensuring compliance with regulatory standards and industry practices.

Generating detailed, accurate reports with contextual analysis and insights.

Solution Provided

An AI-powered financial reporting system was developed using advanced data aggregation tools and Natural Language Generation (NLG) technology. The solution was designed to:

Collect and consolidate financial data from multiple systems and databases.

Analyze data to detect trends, anomalies, and key performance indicators (KPIs).

Generate professional-quality financial reports in real time with contextual narratives.

Development Steps

Data Collection

Connected to financial systems, ERP platforms, and databases to aggregate data related to revenue, expenses, assets, and liabilities.

Preprocessing

Standardized data formats, resolved inconsistencies, and ensured compliance with financial reporting standards.

Model Development

Built AI models to analyze financial data, identify patterns, and calculate KPIs. Integrated NLG algorithms to transform data into coherent and contextually accurate narratives.

Validation

Tested the system by comparing generated reports with manually created ones to ensure accuracy and reliability.

Deployment

Implemented the solution across the organization, providing real-time reporting capabilities to finance teams and executives.

Continuous Monitoring & Improvement

Established a feedback loop to refine algorithms based on user inputs and evolving reporting requirements.

Results

Reduced Reporting Time

Automated workflows reduced the time required to generate financial reports by 70%, enabling faster decision-making.

Minimized Human Errors

AI-driven data aggregation and analysis eliminated manual errors, ensuring consistent and accurate reporting.

Real-Time Financial Insights

The system provided instant access to financial metrics and trends, supporting proactive business strategies.

Improved Compliance

Automated checks ensured compliance with regulatory standards and reduced the risk of reporting inaccuracies.

Enhanced Productivity

Finance teams were freed from repetitive tasks, allowing them to focus on strategic financial planning and analysis.

This record provides an overview of the scope and research output of NESP Marine Biodiversity Hub Project B3 - "Enhancing access to relevant marine information –developing a service for searching, …Show full descriptionThis record provides an overview of the scope and research output of NESP Marine Biodiversity Hub Project B3 - "Enhancing access to relevant marine information –developing a service for searching, aggregating and filtering collections of linked open marine data". For specific data outputs from this project, please see child records associated with this metadata. This project aims to improve the searchability and delivery of sources of linked open data, and to provide the ability to forward collections of discovered data to web services for subsequent processing through the development of a linked open data search tool. This work will improve access to existing data collections, and facilitate the development of new applications by acting as an aggregator of links to streams of marine data. The work will benefit managers (i.e. Department of the Environment staff) by providing fast and simple access to a wide range of marine information products, and offering a means of quickly synthesizing and aggregating multiple sources of information. Planned Outputs • Delivery of open source code to perform the search functions described above. • A simple initial web interface for performing the search and retrieval of results. • Expanded collections of data holdings available in linked open format, including the use of semantic mark-up to enable fully-automated data aggregation and web services. In particular, addition of linked-open data capability to a pilot collection of existing data sets (GA, CERF and NERP data sets).

This pie chart displays public companies per employee type using the aggregation count and is filtered where the company is Miracle Automation Engineering Co.Ltd. The data is about companies.

The global PON Aggregation Extender market is valued at USD XXX million in 2025 and is expected to reach USD XXX million by 2033, growing at a CAGR of XX% from 2025 to 2033. The increasing demand for bandwidth-intensive applications, such as video streaming, gaming, and cloud computing, is driving the growth of the PON Aggregation Extender market. Additionally, the deployment of fiber-to-the-home (FTTH) and fiber-to-the-business (FTTB) networks is also contributing to the growth of the market. Factors restraining the growth of the PON Aggregation Extender market include the high cost of deployment and the lack of skilled technicians. However, the development of new technologies, such as low-cost PON Aggregation Extenders and automated deployment tools, is expected to mitigate these challenges and drive the growth of the market in the coming years. The key players in the PON Aggregation Extender market include AD-net Technology, Catacomm Corporation, Commscope, Huawei, Geodesia, Altice Labs India, Ciena, Advanced Media Technologies, Tejas Networks, Baudcom, Hangzhou CNCR-IT, Sino-Telecom Technology, Genew Technologies, DPTICOME, and Guangzhou Visint Communication Technology.

The size of the Industrial Analytics Market was valued at USD 14.36 Billion in 2023 and is projected to reach USD 41.05 Billion by 2032, with an expected CAGR of 16.19% during the forecast period. The Industrial Analytics Market is experiencing significant growth, driven by the increasing adoption of data-driven decision-making across various industries. Companies are leveraging advanced analytics to optimize operations, enhance productivity, and reduce costs. The integration of Internet of Things (IoT) devices and sensors has further propelled this trend, enabling real-time data collection and analysis. Manufacturing, energy, and logistics sectors are particularly benefiting from these technologies, leading to improved supply chain management and predictive maintenance capabilities. The market is also witnessing a surge in the use of artificial intelligence and machine learning algorithms, which are enhancing the accuracy and efficiency of industrial processes. 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MosaicML offers an LLM that businesses can inexpensively train and fine-tune using their own data., The offering, according to Databricks, provides service providers with a comprehensive view of their networks, operations, and user interactions while safeguarding data privacy and preventing the disclosure of sensitive information. It also serves as a unified foundation for the development of AI and data. It integrates machine learning and generative AI tools with data governance, sharing, and management. The organization has been progressively expanding its collection of vertical lakehouses ever since its retail platform debuted two years ago. In addition, industry-specific platforms for the retail, media, public sector, healthcare, and manufacturing sectors are available., SICK introduced an Industry 4.0 on-premise data intelligence platform in October 2023, which enables logistics and manufacturing organizations to optimize operational performance. 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The future growth rate of the industrial analytics market in the global market is mentioned., Software providers , Network Solutions providers , Consumer goods and retail units , Logistics solutions , Research firms , Software investors , Software Developers , IT enablers , Database , . Key drivers for this market are: Increasing need for data-driven decision-making Growing adoption of IoT and connected devices Government initiatives to promote digital transformation Advancements in AI and cloud computing technologies. Potential restraints include: Data security and privacy concerns Lack of skilled professionals Integration challenges with existing systems. Notable trends are: Augmented reality (AR) and virtual reality (VR) for data visualization Edge computing for real-time data processing Blockchain for secure data sharing.

Replication Package for the paper "An Empirical Investigation of Relevant Changes and Automation Needs in Modern Code Review"

Structure

project_raw_data/ gerrit_review_comments.csv gerrit_review_changes.csv

survey_raw_data/ google_forms_survey.pdf google_forms_survey.csv

RQ1_taxonomy_mcr/ RQ1_inception_phase/ initial_taxonomy.pdf intermediate_taxonomy.pdf

RQ1_definition_phase/
  Q1.2_evaluation_survey.csv
  cram_classified.csv
  cram.pdf

RQ2_automation_needs/ Q2.1-Q2.5_evaluation_survey.xlsx Q2.6-Q2.7_evaluation_survey.xlsx Q2.1-Q2.7_question_index.csv

Now "RQ3_automated_support/" contain the results concerning RQ2.1 in the paper. content explained in the README.md file located in "RQ3_automated_support/README.md"

Contents of the Replication Package

project-raw-data/ contains the data used for the creation of our taxonomies, it includes information about the ten open-source projects.

gerrit_review_comments.csv - information about all in-line review comments used for this paper

gerrit_review_changes.csv - information about all patches analyzed that contain the in-line comments

survey_raw_data/ contains information about the survey conducted for the paper.

google_forms_survey.pdf - the distributed Google Forms of our survey

google_forms_survey.csv - all survey answers obtained from 52 survey participants

RQ1_taxonomy_mcr/ contains information and data about the elicited taxonomies in our paper (RQ1).

RQ1_inception_phase/

initial_taxonomy.pdf - initial taxonomy obtained in the inception phase of our paper

intermediate_taxonomy.pdf - intermediate taxonomy after integrating and merging the initial taxonomy with the one by Beller et al [1]

RQ1_definition_phase/

Q1.2_evaluation_survey.csv - relevant survey feedback with additional taxonomy categories integrated into CRAM

cram_classified.csv - classified review comments (gerrit_review_comments.csv) into CRAM

cram.pdf - CRAM taxonomy

cram_classified_with_frequency_information2020.xls - it contain the information used to compute the frequency of CRAM changes, derived by the analysis of the 211 commits

RQ2_automation_needs/ contains the encoded evaluation of the survey question Q2.1-2.7 for RQ2

Q2.1-Q2.5_evaluation_survey.xlsx - the encoded evaluation of the survey questions Q2.1-Q2.5 (used for the Automation Needs Section in the paper) and contains the following sheets:

all Findings: Very detailed findings matrix distilled from all answers concerning possible automated solutions or general possibilities to achieve automation in MCR. Every feedback was analyzed and decomposed into single findings. These findings are grouped, into categories of our Taxonomy of Code Changes in MCR (CRAM). Red represent in the feedback-text where the corresponding category was distilled from.

unique Findings: As one participant could mention the same categories/solutions in multiple feedbacks, the following matrix is cleaned of any duplication of participant answers. Multiple feedbacks containing the same information by one participant were removed, leaving only distinct occurances.

aggregated by Solution: Aggregated feeback clustered into abstracted solutions and the number of times participants mentioned the solution.

aggregated by Taxonomy: Aggregated feeback grouped by low-level categoried in CRAM.

Q2.6-Q2.7_evaluation_survey.xlsx - the encoded evaluation of the survey questions Q2.6-Q2.7 (used for the Automation Needs Section in the paper) and contains the following sheets:

all Findings: Very detailed findings matrix distilled from all answers concerning possible techniques, approaches and data to achieve automation in MCR. Every feedback was analyzed and decomposed into single findings. These findings are grouped, into categories of our Taxonomy of Code Changes in MCR (CRAM). Red represent in the feedback-text where the corresponding category was distilled from.

unique Findings: As one participant could mention the same categories/solutions in multiple feedbacks, the following matrix is cleaned of any duplication of participant answers. Multiple feedbacks containing the same information by one participant were removed, leaving only distinct occurances.

aggregated by low-level taxonomy: Aggregated mentionings of approaches/data by developers in the survey grouped by low-level taxonomy category.

aggregated by high-level taxonomy: Aggregated mentionings of approaches/data by developers in the survey grouped by high-level taxonomy category.

Q2.1-Q2.7_question_index.csv - table of IDs given to each participant-question pair for Q2.1-Q2.7 in order to trace back the feeback.

cram_survey-with_criticality_and_feasibility2020.xls and cram_survey-with_relevance_and_completeness_information2020.xls: they contain we results of the survey, involving 14 additional participants (12 developers and 2 researchers), not involved in the aforementioned survey, and performed to qualitatively assess the relevance and completeness of the identified MCR change types as well as assess how critical and feasible to implement are some of the identified techniques to support MCR activities.

RQ2_1_automated_support/ (or RQ3_automated_support/ )- content explained in the README.md file located in "RP_EMSE_MCR_2019/tree/master/EMSE_MCR_2019/RQ3_automated_support/README.md"

References

[1] Moritz Beller, Alberto Bacchelli, Andy Zaidman, and Elmar Juergens. 2014. Modern code reviews in open-source projects: which problems do they fix?. In Proceedings of the 11th Working Conference on Mining Software Repositories (MSR 2014). ACM, New York, NY, USA, 202-211. DOI: http://dx.doi.org/10.1145/2597073.2597082

The global public relations (PR) tools market size was valued at USD 12.7 Billion in 2024. Looking forward, IMARC Group estimates the market to reach USD 28.9 Billion by 2033, exhibiting a CAGR of 9.12% during 2025-2033. Asia Pacific currently dominates the market, holding a market share of over 36.5% in 2024. The public relations tools market share is driven by increasing demand for real-time communication, growing digitalization, and the rise of social media platforms. Additionally, data analytics and automation are boosting market growth.

IMARC Group provides an analysis of the key trends in each segment of the global public relations (PR) tools market, along with forecast at the global, regional, and country levels from 2025-2033. The market has been categorized based on solution, deployment, application, and industry.

This pie chart displays public companies per CEO gender using the aggregation count and is filtered where the company is Miracle Automation Engineering Co.Ltd. The data is about companies.