The various performance criteria applied in this analysis include the probability of reaching the ultimate target, the costs, elapsed times and system vulnerability resulting from any intrusion. This Excel file contains all the logical, probabilistic and statistical data entered by a user, and required for the evaluation of the criteria. It also reports the results of all the computations.

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

*** Fake News on Twitter ***

These 5 datasets are the results of an empirical study on the spreading process of newly fake news on Twitter. Particularly, we have focused on those fake news which have given rise to a truth spreading simultaneously against them. The story of each fake news is as follow:

1- FN1: A Muslim waitress refused to seat a church group at a restaurant, claiming "religious freedom" allowed her to do so.

2- FN2: Actor Denzel Washington said electing President Trump saved the U.S. from becoming an "Orwellian police state."

3- FN3: Joy Behar of "The View" sent a crass tweet about a fatal fire in Trump Tower.

4- FN4: The animated children's program 'VeggieTales' introduced a cannabis character in August 2018.

5- FN5: In September 2018, the University of Alabama football program ended its uniform contract with Nike, in response to Nike's endorsement deal with Colin Kaepernick.

The data collection has been done in two stages that each provided a new dataset: 1- attaining Dataset of Diffusion (DD) that includes information of fake news/truth tweets and retweets 2- Query of neighbors for spreaders of tweets that provides us with Dataset of Graph (DG).

DD

DD for each fake news story is an excel file, named FNx_DD where x is the number of fake news, and has the following structure:

The structure of excel files for each dataset is as follow:

Each row belongs to one captured tweet/retweet related to the rumor, and each column of the dataset presents a specific information about the tweet/retweet. These columns from left to right present the following information about the tweet/retweet:

User ID (user who has posted the current tweet/retweet)

The description sentence in the profile of the user who has published the tweet/retweet

The number of published tweet/retweet by the user at the time of posting the current tweet/retweet

Date and time of creation of the account by which the current tweet/retweet has been posted

Language of the tweet/retweet

Number of followers

Number of followings (friends)

Date and time of posting the current tweet/retweet

Number of like (favorite) the current tweet had been acquired before crawling it

Number of times the current tweet had been retweeted before crawling it

Is there any other tweet inside of the current tweet/retweet (for example this happens when the current tweet is a quote or reply or retweet)

The source (OS) of device by which the current tweet/retweet was posted

Tweet/Retweet ID

Retweet ID (if the post is a retweet then this feature gives the ID of the tweet that is retweeted by the current post)

Quote ID (if the post is a quote then this feature gives the ID of the tweet that is quoted by the current post)

Reply ID (if the post is a reply then this feature gives the ID of the tweet that is replied by the current post)

Frequency of tweet occurrences which means the number of times the current tweet is repeated in the dataset (for example the number of times that a tweet exists in the dataset in the form of retweet posted by others)

State of the tweet which can be one of the following forms (achieved by an agreement between the annotators):

r : The tweet/retweet is a fake news post

a : The tweet/retweet is a truth post

q : The tweet/retweet is a question about the fake news, however neither confirm nor deny it

n : The tweet/retweet is not related to the fake news (even though it contains the queries related to the rumor, but does not refer to the given fake news)

DG

DG for each fake news contains two files:

A file in graph format (.graph) which includes the information of graph such as who is linked to whom. (This file named FNx_DG.graph, where x is the number of fake news)

A file in Jsonl format (.jsonl) which includes the real user IDs of nodes in the graph file. (This file named FNx_Labels.jsonl, where x is the number of fake news)

Because in the graph file, the label of each node is the number of its entrance in the graph. For example if node with user ID 12345637 be the first node which has been entered into the graph file then its label in the graph is 0 and its real ID (12345637) would be at the row number 1 (because the row number 0 belongs to column labels) in the jsonl file and so on other node IDs would be at the next rows of the file (each row corresponds to 1 user id). Therefore, if we want to know for example what the user id of node 200 (labeled 200 in the graph) is, then in jsonl file we should look at row number 202.

The user IDs of spreaders in DG (those who have had a post in DD) would be available in DD to get extra information about them and their tweet/retweet. The other user IDs in DG are the neighbors of these spreaders and might not exist in DD.

This dataset includes two excel sheets. The first contains vegetation data ("species_data", a matrix of 668 plots x 213 species) and the second contains plant functional traits data ("traits_data") that were used to evaluate temporal changes in taxonomic and functional diversity of Mediterranean coastal dune habitats.

As to the first sheet ("species_data"): vegetation data were collected at two points in time (Time 0, hereafter T₀: 2002-2007, and Time 1, herafter T₁: 2017-2018) in 334 randomly-sampled, georeferenced, standardized (4 m²) plots. Historical data used for the resurveying study were extracted from RanVegDunes (Sperandii et al. 2017). Details on the resurveying protocol can be found in Sperandii et al. (2019), but in short: resampling activities took place during the same months in which the original sampling was done, and plot positions were relocated using a GPS unit on which historical geographic coordinates were stored. Plots are located in coastal dune sites along the Tyrrhenian and Adriatic coasts of Central Italy, and belong to herbaceous communities classified into the following EU Habitats (sensu Annex I 92/43/EEC): upper beach (Habitat 1210), embryo dunes (Habitat 2110), shifting dunes (Habitat 2120), fixed dunes (Habitat 2210), and dune grasslands (Habitat 2230). A subset of plots could not be classified into an EU Habitat because they were highly disturbed or invaded by alien species (“NC-plots”). The matrix includes cover data, expressed as percentage (%) cover.

As to the second sheet ("traits_data"): this sheet includes data on 3 plant functional traits, two of them quantitative (plant height, specific leaf area - SLA) and one qualitative (plant lifespan). Data for the quantitative traits represent species-level average trait values and were extracted from “TraitDunes”, a database registered on the global platform TRY (Kattge et al., 2020). Functional trait data were collected in the same sites covered by the resurveying study. Functional trait data were originally measured on the most abundant species, and are available for a varying number of species depending on the trait.

References:

Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., ... & Wirth, C. (2020). TRY plant trait database–enhanced coverage and open access. Global Change Biology.

Sperandii, M.G., Prisco, I., Stanisci, A., & Acosta, A.T.R (2017). RanVegDunes-A random plot database of Italian coastal dunes. Phytocoenologia, 47(2), 231-232.

Sperandii, M.G., Bazzichetto, M., Gatti, F., & Acosta, A.T.R. (2019). Back into the past: Resurveying random plots to track community changes in Italian coastal dunes. Ecological Indicators, 96, 572-578.

The Hadith Isnad narrators data may be useful for the researcher to better adapt these techniques for particular problems. This data is developed for future research on the public repository for all the Research Institutes, Scientific and Islamic communities who want to work on Hadith's domain. This dataset contains two types of excel documents: Hadith_SahihMuslim_CoreInfo.xlsx file (7748 records) and Hadith_SahihMuslim_DetailsInfo_Sanad_Narrators.xlsx document (77797 records). The data contains 7748 Hadiths and 2092 unique records of Narrators of All Sahih Muslim Hadith

As important carriers of innovation activities, patents, sci-tech achievements and papers play an increasingly prominent role in national political and economic development under the background of a new round of technological revolution and industrial transformation. However, in a distributed and heterogeneous environment, the integration and systematic description of patents, sci-tech achievements and papers data are still insufficient, which limits the in-depth analysis and utilization of related data resources. The dataset of knowledge graph construction for patents, sci-tech achievements and papers is an important means to promote innovation network research, and is of great significance for strengthening the development, utilization, and knowledge mining of innovation data. This work collected data on patents, sci-tech achievements and papers from China's authoritative websites spanning the three major industries—agriculture, industry, and services—during the period 2022-2025. After processes of cleaning, organizing, and normalization, a patents-sci-tech achievements-papers knowledge graph dataset was formed, containing 10 entity types and 8 types of entity relationships. To ensure quality and accuracy of data, the entire process involved strict preprocessing, semantic extraction and verification, with the ontology model introduced as the schema layer of the knowledge graph. The dataset establishes direct correlations among patents, sci-tech achievements and papers through inventors/contributors/authors, and utilizes the Neo4j graph database for storage and visualization. The open dataset constructed in this study can serve as important foundational data for building knowledge graphs in the field of innovation, providing structured data support for innovation activity analysis, scientific research collaboration network analysis and knowledge discovery.The dataset consists of two parts. The first part includes three Excel tables: 1,794 patent records with 10 fields, 181 paper records with 7 fields, and 1,156 scientific and technological achievement records with 11 fields. The second part is a knowledge graph dataset in CSV format that can be imported into Neo4j, comprising 10 entity files and 8 relationship files.

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

Graph Database for Telecom Networks Market Outlook

According to our latest research, the global graph database for telecom networks market size is valued at USD 1.34 billion in 2024, reflecting a robust adoption rate across the telecom sector. The market is experiencing a strong upward trajectory with a CAGR of 22.7% from 2025 to 2033. By 2033, the market is projected to reach a substantial USD 10.15 billion, driven by the increasing complexity of telecom networks and the urgent need for advanced data management and analytics solutions. The primary growth factor is the surging demand for real-time network analytics and fraud detection capabilities, which are critical for telecom operators seeking operational efficiency and competitive advantage.

The rapid proliferation of connected devices, 5G rollouts, and the exponential growth of data traffic are fundamentally transforming the telecom industry landscape. Telecom networks are evolving into highly complex, dynamic ecosystems that generate vast amounts of interconnected data. Traditional relational databases are often inadequate for handling such intricate relationships and real-time analytics requirements. Graph database solutions are uniquely positioned to address these challenges by enabling telecom operators to model, analyze, and visualize complex network topologies, customer interactions, and transactional data with unparalleled speed and flexibility. This technological shift is a key growth driver, as telecom providers increasingly seek scalable, agile, and intelligent data management platforms to enhance customer experience, optimize network performance, and accelerate digital transformation initiatives.

Another significant growth factor for the graph database for telecom networks market is the escalating threat landscape, particularly in the domain of fraud detection and cybersecurity. Telecom operators are frequent targets of sophisticated fraud schemes, including SIM card cloning, subscription fraud, and network intrusion attempts. Graph databases excel at identifying hidden patterns, relationships, and anomalies within massive datasets, enabling telecom companies to detect and mitigate fraud in real time. The ability to perform advanced analytics on interconnected data sets is empowering telecom operators to proactively safeguard their networks, reduce financial losses, and comply with stringent regulatory requirements. As the complexity of cyber threats intensifies, the adoption of graph database solutions for security and fraud prevention is expected to surge, further fueling market growth.

The growing emphasis on customer-centricity and personalized service delivery is also propelling market expansion. Telecom operators are leveraging graph databases to gain a 360-degree view of customer journeys, preferences, and interactions across multiple touchpoints. This holistic understanding facilitates targeted marketing, churn prediction, and tailored service offerings, which are essential for customer retention and revenue growth in a highly competitive market. The convergence of telecom networks with emerging technologies such as artificial intelligence, machine learning, and the Internet of Things (IoT) is amplifying the need for graph-based analytics, as these technologies rely on real-time, context-aware insights derived from complex data relationships. As a result, the integration of graph databases into telecom network architectures is becoming a strategic imperative for industry leaders.

From a regional perspective, North America currently leads the global graph database for telecom networks market, accounting for the largest revenue share in 2024. The region’s dominance is attributed to the early adoption of advanced analytics technologies, robust digital infrastructure, and the presence of major telecom and technology companies. Asia Pacific is emerging as the fastest-growing region, driven by massive investments in 5G networks, expanding mobile subscriber base, and increasing focus on digital transformation across telecom operators. Europe is also witnessing significant adoption of graph database solutions, particularly in the context of regulatory compliance and network optimization. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, supported by ongoing telecom sector modernization and rising demand for advanced data analytics. The global market outlook remains highly promising, with all regions poised to contribute to sustained growth over the forecast period.<b

This dataset contains in-air hand-written numbers and shapes data used in the paper:B. Alwaely and C. Abhayaratne, "Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition," in IEEE Access, vol. 7, pp. 159661-159673, 2019, doi: 10.1109/ACCESS.2019.2950643.The dataset contains the following:-Readme.txt- InAirNumberShapeDataset.zip containing-Number Folder (With 2 sub folders for Matlab and Excel)-Shapes Folder (With 2 sub folders for Matlab and Excel)The datasets include the in-air drawn number and shape hand movement path captured by a Kinect sensor. The number sub dataset includes 500 instances per each number 0 to 9, resulting in a total of 5000 number data instances. Similarly, the shape sub dataset also includes 500 instances per each shape for 10 different arbitrary 2D shapes, resulting in a total of 5000 shape instances. The dataset provides X, Y, Z coordinates of the hand movement path data in Matlab (M-file) and Excel formats and their corresponding labels.This dataset creation has received The University of Sheffield ethics approval under application #023005 granted on 19/10/2018.

his project involves the creation of an interactive Excel dashboard for SwiftAuto Traders to analyze and visualize car sales data. The dashboard includes several visualizations to provide insights into car sales, profits, and performance across different models and manufacturers. The project makes use of various charts and slicers in Excel for the analysis.

Objective: The primary goal of this project is to showcase the ability to manipulate and visualize car sales data effectively using Excel. The dashboard aims to provide:

Profit and Sales Analysis for each dealer. Sales Performance across various car models and manufacturers. Resale Value Analysis comparing prices and resale values. Insights into Retention Percentage by car models. Files in this Project: Car_Sales_Kaggle_DV0130EN_Lab3_Start.xlsx: The original dataset used to create the dashboard. dashboards.xlsx: The final Excel file that contains the complete dashboard with interactive charts and slicers. Key Visualizations: Average Price and Year Resale Value: A bar chart comparing the average price and resale value of various car models. Power Performance Factor: A column chart displaying the performance across different car models. Unit Sales by Model: A donut chart showcasing unit sales by car model. Retention Percentage: A pie chart illustrating customer retention by car model. Tools Used: Microsoft Excel for creating and organizing the visualizations and dashboard. Excel Slicers for interactive filtering. Charts: Bar charts, pie charts, column charts, and sunburst charts. How to Use: Download the Dataset: You can download the Car_Sales_Kaggle_DV0130EN_Lab3_Start.xlsx file from Kaggle and follow the steps to create a similar dashboard in Excel. Open the Dashboard: The dashboards.xlsx file contains the final version of the dashboard. Simply open it in Excel and start exploring the interactive charts and slicers.

Graph Database-as-a-Service Market Outlook

According to our latest research, the global Graph Database-as-a-Service market size reached USD 2.1 billion in 2024, reflecting a robust expansion across multiple industries. The market is exhibiting a strong compound annual growth rate (CAGR) of 25.6%, and is projected to attain a value of USD 15.2 billion by 2033. This impressive growth trajectory is primarily driven by the increasing demand for highly scalable, flexible, and cloud-native data management solutions that can efficiently handle complex, interconnected datasets. The proliferation of digital transformation initiatives, surging adoption of advanced analytics, and the critical need for real-time data insights are further propelling the market forward, as organizations across sectors strive to optimize operations and unlock new business opportunities through graph-based technologies.

A significant factor fueling the expansion of the Graph Database-as-a-Service market is the escalating complexity of enterprise data environments. Traditional relational databases are often ill-equipped to manage the intricate relationships and dynamic data structures prevalent in modern business contexts. As a result, organizations are turning to graph databases for their ability to model, store, and analyze highly connected data efficiently. The rise of artificial intelligence, machine learning, and big data analytics has also intensified the need for data platforms that can seamlessly integrate with these technologies. Graph Database-as-a-Service solutions, with their cloud-native architecture and managed service offerings, enable businesses to rapidly deploy, scale, and maintain graph databases without the overhead of on-premises infrastructure, thus accelerating innovation and reducing operational costs.

Another key growth driver is the surge in demand for real-time analytics and personalized customer experiences across industries such as BFSI, retail, healthcare, and telecommunications. Graph databases excel at uncovering hidden patterns, detecting fraud, and enabling recommendation engines, which are critical for delivering tailored services and mitigating risks. Enterprises are leveraging Graph Database-as-a-Service platforms to enhance customer analytics, streamline risk and compliance management, and optimize network and IT operations. The flexibility of deployment models—including public, private, and hybrid cloud—further amplifies adoption, as organizations can select the architecture that best aligns with their security, scalability, and regulatory requirements. The integration of graph databases with existing IT ecosystems and the availability of robust APIs and developer tools are making it increasingly accessible for businesses of all sizes to harness the power of connected data.

From a regional perspective, North America continues to dominate the Graph Database-as-a-Service market, owing to its advanced technological infrastructure, early adoption of cloud computing, and a vibrant ecosystem of innovative startups and established enterprises. Europe is witnessing rapid growth, driven by stringent data privacy regulations and the increasing digitalization of industries. The Asia Pacific region is emerging as a significant growth engine, propelled by the expansion of e-commerce, financial services, and healthcare sectors, coupled with substantial investments in digital transformation initiatives. As organizations worldwide recognize the strategic value of graph data management, the market is expected to experience widespread adoption across both developed and emerging economies, with tailored solutions catering to diverse industry verticals and regulatory landscapes.

Component Analysis

The Graph Database-as-a-Service market is segmented by component into software and services, each playing a pivotal role in shaping the overall market dynamics. The software segment encompasses the core graph database platforms and associated tools that facilitate data modeling, querying, visualization, and integration. These platforms are designed to deliver high performance, scalability, and ease of use, enabling organizations to manage complex relationships and large volumes of interconnected data seamlessly. Leading vendors are continuously innovating, introducing advanced features such as multi-model support, enhanced security, and automated scaling, which are driving widespread adoption across various industry verticals. The software component is particularly critical for enterprise

This dataset contains all the files used in developing the Tree-KG, the knowledge graph to capture the tree annotations in the works of Vladimir Nabokov.

In the Ontology Versions folder, four ontology (TAV) files in turtle (.ttl) format are provided. They are all numbered and dated to represent their different versions. Also, the competency questions (CQs) and sample SPARQL queries provided in .txt files. The KG was developed on Protégé.

(1) contains the schema for the TAV vocabulary without the linking to external vocabularies.

(2) contains the schema for TAV vocabulary with the links to external terms.

(3) contains the Tree-KG along with the data from three Nabokov novels (Mary; King, Queen, Knave; Glory) in a self-contained way.

(4) contains the Tree-KG that reflects the data from three novels (Mary; King, Queen, Knave; Glory) in a linked data way.

(5) contains some of the CQs used to develop TAV (.txt) file.

(6) contains some sample SPARQL queries (.txt) file.

In the Trees of Nabokov-Annotated Dataset folder, 6 spreadsheets in excel (.xlsx) format are provided. They are numbered. Note that annotated data are all in English as the consulted works are the English translations of the literary works of Nabokov.

(1) contains the tree annotations from the novels originally written in Russian by Vladimir Nabokov.

(2) contains the tree annotations from the novels originally written in English by Vladimir Nabokov.

(3) contains the tree annotations from the short stories originally written in Russian and English by Vladimir Nabokov.

(4) is the knowledge base (KB) developed to link the annotated trees to Wikidata and DBPedia.

(5) is the benchmarking results of some entity recognition tools. It also includes the relevant passages from Nabokov's novels that were used in the experiments.

(6) represents the complete bibliographic details of the works of Vladimir Nabokov (https://thenabokovian.org/abbreviations).

Graph Database for Telecom Networks Market Outlook

According to our latest research, the global market size for Graph Database for Telecom Networks in 2024 stands at USD 1.47 billion, with a robust compound annual growth rate (CAGR) of 22.1% projected from 2025 to 2033. By the end of 2033, the market is expected to reach USD 7.02 billion. This remarkable growth is primarily fueled by the increasing complexity of telecom networks, the proliferation of connected devices, and the urgent need for real-time data processing and analytics to drive operational efficiency and competitive differentiation. As per our latest research, the adoption of graph database technologies is accelerating in the telecom sector, enabling organizations to address challenges related to data interconnectivity, fraud detection, and network optimization.

One of the most significant growth factors in the Graph Database for Telecom Networks market is the exponential rise in data generated by telecom networks, driven by the widespread adoption of 5G technology, IoT devices, and digital transformation initiatives. Telecom operators are increasingly leveraging graph databases to model and manage complex relationships between network elements, subscribers, and services. These databases enable organizations to gain a holistic view of their networks, streamline network management processes, and quickly identify and resolve issues. The ability of graph databases to handle dynamic, highly connected data structures gives telecom operators a strategic advantage in managing network topologies, optimizing routing, and delivering superior customer experiences. As the volume and complexity of telecom data continue to surge, the demand for advanced graph database solutions is expected to grow at a rapid pace, underpinning the market's impressive CAGR.

Another critical driver for the Graph Database for Telecom Networks market is the increasing emphasis on fraud detection and prevention. Telecom networks are frequent targets for sophisticated fraud schemes, including subscription fraud, SIM card cloning, and international revenue share fraud. Traditional relational databases often fall short in detecting complex fraud patterns that span multiple entities and relationships. In contrast, graph databases excel at uncovering hidden connections and suspicious activity in real-time, enabling telecom operators to proactively mitigate risks and reduce financial losses. By integrating graph analytics with machine learning algorithms, telecom companies can enhance their ability to detect anomalies, improve security, and comply with regulatory requirements. This growing need for advanced fraud detection capabilities is a key factor propelling the adoption of graph database technologies in the telecom industry.

The evolution of customer analytics and personalized service offerings is also playing a pivotal role in driving the Graph Database for Telecom Networks market. Telecom operators are increasingly focused on delivering tailored services and experiences to retain customers and increase revenue. Graph databases empower organizations to analyze customer interactions, preferences, and behavior across multiple touchpoints, enabling hyper-personalized marketing, targeted upselling, and improved customer support. The ability to map and analyze complex customer journeys in real-time allows telecom companies to identify high-value segments, predict churn, and design effective retention strategies. As customer expectations continue to rise, the adoption of graph database solutions for advanced analytics and personalized service delivery is expected to accelerate, further fueling market expansion.

Regionally, the Graph Database for Telecom Networks market is witnessing significant growth in Asia Pacific, North America, and Europe, with emerging economies in Latin America and the Middle East & Africa also showing considerable potential. North America currently leads the market, driven by the presence of major telecom operators, advanced network infrastructure, and early adoption of cutting-edge technologies. Asia Pacific is projected to exhibit the highest CAGR during the forecast period, supported by rapid digitalization, expanding mobile subscriber base, and substantial investments in 5G and IoT deployments. Europe remains a key market, benefiting from regulatory initiatives, strong R&D capabilities, and a mature telecom ecosystem. As telecom operators across regions strive to modernize their netw

Service Topology Graph Database Market Outlook

According to our latest research, the global service topology graph database market size reached USD 1.42 billion in 2024, demonstrating robust momentum with a compound annual growth rate (CAGR) of 21.8%. The market is expected to achieve a value of USD 10.62 billion by 2033. This impressive growth is primarily driven by the increasing demand for advanced data management solutions, the proliferation of complex IT infrastructures, and the rising necessity for real-time analytics and visualization across diverse industries. The market’s rapid expansion is further bolstered by technological advancements in graph database architectures and the growing adoption of cloud-based deployment models.

One of the most significant growth factors in the service topology graph database market is the escalating complexity of modern IT environments. As organizations transition toward hybrid and multi-cloud infrastructures, the need for solutions that can accurately map and manage intricate service relationships has become paramount. Graph databases excel at representing highly interconnected data, making them ideal for modeling service topologies. This capability enables enterprises to visualize dependencies, identify bottlenecks, and optimize resource allocation, thereby enhancing operational efficiency and minimizing downtime. Additionally, the growing integration of artificial intelligence and machine learning with graph databases allows for predictive analytics and automated anomaly detection, further fueling market growth.

Another key driver is the surge in demand for enhanced network management and security. With the increasing frequency and sophistication of cyber threats, organizations are seeking comprehensive solutions to monitor and secure their networks. Service topology graph databases provide unparalleled visibility into network structures, enabling proactive identification of vulnerabilities and facilitating rapid incident response. These databases support real-time monitoring and compliance tracking, which are critical for industries with stringent regulatory requirements such as BFSI and healthcare. The ability to correlate data from multiple sources and uncover hidden patterns is proving invaluable for security teams, making graph databases an essential component of modern cybersecurity strategies.

The expanding adoption of digital transformation initiatives across various sectors also contributes to the market’s growth. Enterprises are leveraging service topology graph databases to streamline asset management, optimize IT operations, and improve customer experiences. In the retail sector, for example, these databases help map customer journeys and personalize interactions by analyzing relationships between products, users, and transactions. In manufacturing, they facilitate predictive maintenance and supply chain optimization by modeling equipment dependencies and process flows. As organizations continue to prioritize data-driven decision-making, the demand for graph-based solutions is expected to rise significantly, further propelling the market forward.

From a regional perspective, North America currently leads the global market, accounting for the largest revenue share in 2024. This dominance is attributed to the presence of major technology vendors, early adoption of advanced IT solutions, and significant investments in research and development. Europe follows closely, driven by stringent data privacy regulations and the need for efficient compliance management. The Asia Pacific region is witnessing the fastest growth, fueled by rapid digitalization, expanding IT infrastructure, and increasing investments in cloud computing. Latin America and the Middle East & Africa are also experiencing steady growth, supported by government initiatives to modernize public services and enhance cybersecurity capabilities.

Component Analysis

The component segment of the service topology graph database market is bifurcated into software and services, each playing a pivotal role in driving overall market expansion. The software sub-segment dominates the market, owing to the continuous evolution of graph database platforms that offer enhanced scalability, flexibility, and integration capabilities. Modern graph database software solutions are equipped with advanced visualization tools, intuitive user interfaces, and robust APIs, enabling seamless in

This repository contains a collection of data about 454 value chains from 23 rural European areas of 16 countries. This data is obtained through a semi-automatic workflow that transforms raw textual data from an unstructured MS Excel sheet into semantic knowledge graphs.In particular, the repository contains:MS Excel sheet containing different value chains details provided by MOuntain Valorisation through INterconnectedness and Green growth (MOVING) European project;454 CSV files containing events, titles, entities and coordinates of narratives of each value chain, obtained by pre-processing the MS Excel sheet454 Web Ontology Language (OWL) files. This collection of files is the result of the semi-automatic workflow, and is organized as a semantic knowledge graph of narratives, where each narrative is a sub-graph explaining one among the 454 value chains and its territory aspects. The knowledge graph is based on the Narrative Ontology, an ontology developed by Institute of Information Science and Technologies (ISTI-CNR) as an extension of CIDOC CRM, FRBRoo, and OWL Time.Two CSV files that compile all the possible available information extracted from 454 Web Ontology Language (OWL) files.GeoPackage files with the geographic coordinates related to the narratives.The HTML files that show all the different SPARQL and GeoSPARQL queries.The HTML files that show the story maps about the 454 value chains.An image showing how the various components of the dataset interact with each other.

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.