What is the primary focus of the ICBDCSI conference?

The conference focuses on big data, computer science, and information, providing a platform for sharing research and advancements in these fields.

When does the conference begin?

The conference is scheduled to begin in the fall.

International Conference on Big Data Computer Science and Information (ICBDCSI)

Name: International Conference on Big Data Computer Science and Information (ICBDCSI)
Start: 2026-11-12T00:00:00Z
End: 2026-11-12T00:00:00Z
Location: Kharkiv

Nov 12, 2026Kharkiv, UkraineIn-person

Registration open

The International Conference on Big Data Computer Science and Information (ICBDCSI) is set to convene in Kharkiv, Ukraine. This academic gathering focuses on the interdisciplinary fields of big data, computer science, and information technology. It provides a platform for researchers, academics, and industry professionals to share advancements, research findings, and practical experiences.

Committees & topics

Topics
Big Data Techniques, Models and Algorithms · Big Data Infrastructure and Platforms · Big Data Search and Mining · Big Data Security, Privacy and Trust · Big Data Applications in Bioinformatics, Multimedia and Beyond · Big Data Tools and Systems · Big Data Mining · Big Data Management and Governance

Why it matters

The convergence of big data, computer science, and information is a critical area of study, driving innovation across various sectors globally. The insights and technologies discussed at events like ICBDCSI have profound implications for national security, economic development, and societal progress. For instance, advancements in big data analytics can inform policy-making, enhance cybersecurity measures, and optimize resource allocation in complex international scenarios.

Furthermore, the location of this conference in UKR highlights the growing global interest and investment in these technological domains, even in regions facing geopolitical challenges. The exchange of knowledge fostered by such conferences contributes to a collective understanding of emerging threats and opportunities, influencing how nations approach digital transformation and data governance. The discussions can also shed light on the ethical considerations and regulatory frameworks necessary to manage the vast amounts of data being generated and processed worldwide.

How to prepare

Delegates preparing for ICBDCSI should focus on understanding the current landscape of big data applications and their implications for international relations. A strong grasp of concepts like "data governance" and "critical information infrastructure" will be beneficial. Consider how the insights from the conference could be applied to real-world diplomatic challenges, such as leveraging data for conflict resolution or improving humanitarian aid distribution.

Participants should also be prepared to engage with diverse perspectives on data privacy, security, and the geopolitical dimensions of technological advancement. Reviewing lessons such as "dynamic-analysis" can help in understanding the evolving nature of data-driven systems, while exploring "the-power-interest-grid" might offer insights into stakeholder dynamics in the tech policy space. Engaging with these topics will allow for more meaningful contributions to the conference discussions and a deeper appreciation of the complex interplay between technology and global affairs.

Country perspectives

Where the most-relevant 4 countries stand on the core issues in play. Click through for the full country profile.

Ukraine

A host nation with a strong interest in leveraging big data and computer science for national development and security, particularly given its geopolitical context.

Role in topic

UKR serves as the host for this conference, indicating its commitment to fostering technological advancement and academic exchange in critical areas like big data and computer science. Its strategic location and ongoing challenges underscore the importance of robust information systems and data analysis for national resilience and international cooperation.

United States

A global leader in big data and computer science, with significant contributions to research, development, and policy frameworks.

Role in topic

The USA is a major innovator in big data and computer science, with extensive research institutions and tech companies driving advancements. Its perspective often emphasizes open innovation, data-driven economic growth, and the development of international standards for data security and privacy. The USA's involvement in such conferences is crucial for shaping global technological discourse.

China

A rapidly advancing power in big data and AI, with a focus on national strategic applications and extensive data collection.

Role in topic

CHN has made significant strides in big data and computer science, particularly in areas like artificial intelligence and surveillance technologies. Its approach often involves large-scale data collection and analysis for both economic development and national security, presenting a distinct model that often sparks international debate on data governance and human rights.

Germany

A proponent of strong data protection and ethical AI, balancing technological advancement with regulatory oversight.

Role in topic

DEU, a key member of the European Union, often champions stringent data protection regulations and an ethical approach to technological development. Its perspective at such conferences would likely emphasize the importance of privacy-by-design, transparent algorithms, and the responsible use of big data to ensure societal benefits without compromising individual rights.

Topics & background

The background behind each topic and the actors that shape it.

Big Data Techniques, Models and Algorithms

The field of big data analytics emerged in the mid-2000s as the volume, velocity, and variety of digital information began to outpace the capabilities of traditional relational databases and statistical methods. Google's 2004 MapReduce paper and the subsequent open-source Apache Hadoop project (2006) catalyzed a wave of distributed computing models, followed by in-memory frameworks like Apache Spark (2014) and the rise of streaming algorithms for real-time analytics. Parallel advances in machine learning — particularly deep learning after 2012 — created a feedback loop where larger datasets drove better models, and better models created demand for larger datasets. Today the central debates concern algorithmic scalability, energy efficiency, and reproducibility. Researchers are pushing approximate algorithms, federated learning, and graph-based models to handle exabyte-scale workloads, while policymakers in the EU, US, and China increasingly scrutinize how foundational algorithms shape competition and public discourse. Standards bodies such as ISO/IEC JTC 1/SC 42 are working to harmonize definitions of big data analytics and AI lifecycle management, but consensus remains elusive across jurisdictions with divergent data governance philosophies.

Key players

Big Data Infrastructure and Platforms

Big data infrastructure evolved from on-premises Hadoop clusters in the late 2000s to hyperscale cloud platforms operated by Amazon Web Services, Microsoft Azure, Google Cloud, and Alibaba Cloud. The shift to cloud-native architectures — containers, Kubernetes, object storage, and serverless compute — has concentrated global data processing capacity in a small number of providers and jurisdictions. Submarine cables, hyperscale data centers, and specialized AI accelerators (GPUs, TPUs) now constitute critical infrastructure on par with traditional utilities. The contemporary debate centers on data sovereignty, supply-chain resilience, and energy consumption. The EU's Gaia-X initiative, India's Digital Public Infrastructure stack, and China's 'East Data, West Computing' project all reflect efforts to assert national or regional control over compute capacity. Meanwhile, US export controls on advanced semiconductors (October 2022 and subsequent updates) have constrained access to the chips that underpin big-data platforms, reshaping global investment flows and prompting indigenous chip programs in multiple states.

Key players

Big Data Search and Mining

Search and mining technologies trace back to early information retrieval research at institutions like Cornell and Stanford, culminating in Google's PageRank (1998) and the subsequent commercialization of web-scale search. The 2010s brought distributed search engines (Elasticsearch, Solr), vector databases, and semantic search powered by embedding models, while data mining matured from association-rule and clustering techniques into deep representation learning over heterogeneous data sources. The current frontier is retrieval-augmented generation (RAG) and large-scale knowledge graph construction, which combine traditional indexing with generative AI. This raises unresolved questions about copyright (notably the New York Times v. OpenAI litigation filed in December 2023), web-scraping ethics, and the concentration of search markets — a concern reflected in the US Department of Justice's 2023–2024 antitrust case against Google and the EU's Digital Markets Act designations.

Key players

Big Data Security, Privacy and Trust

Concerns about data security and privacy intensified after the 2013 Snowden disclosures, the 2017 Equifax breach affecting 147 million people, and the 2018 Cambridge Analytica revelations. The EU's General Data Protection Regulation (GDPR), in force since May 2018, set a global baseline for personal data protection and inspired analogous laws in Brazil (LGPD, 2020), California (CCPA/CPRA), India (DPDP Act, 2023), and China (PIPL, 2021). At the same time, ransomware and supply-chain attacks — SolarWinds (2020), Colonial Pipeline (2021), MOVEit (2023) — exposed the fragility of large data pipelines. The core dispute is how to reconcile cross-border data flows with national security and privacy. The CJEU's Schrems II ruling (2020) invalidated the EU-US Privacy Shield, and the successor EU-US Data Privacy Framework (2023) faces continued legal challenge. Privacy-enhancing technologies — differential privacy, homomorphic encryption, secure multi-party computation, and federated learning — are advancing rapidly but have not yet resolved the underlying tension between data utility and individual rights.

Key players

Big Data Applications in Bioinformatics, Multimedia and Beyond

Big data applications have moved from back-office analytics into the core of scientific discovery and creative industries. The Human Genome Project (completed 2003) and the subsequent fall in sequencing costs produced petabyte-scale biological datasets, while consortia like UK Biobank and the All of Us Research Program in the US accelerated population-scale genomics. DeepMind's AlphaFold (2020–2022) demonstrated how large datasets and deep learning could solve grand-challenge problems in structural biology. In multimedia, generative models for image, audio, and video have transformed broadcasting, gaming, and advertising. The issues today are ethical and economic. Genomic data raises consent, ancestry, and biosecurity concerns, especially as datasets cross borders. Multimedia applications have triggered disputes over training-data provenance, deepfakes, and creator compensation — reflected in the 2023 Hollywood writers' and actors' strikes and the EU AI Act's provisions on generative AI. Governments are also exploring big data for precision agriculture, climate modeling, and smart-city applications, each carrying its own governance challenges.

Key players

Big Data Tools and Systems

The open-source ecosystem has been the backbone of big data tooling. Apache Hadoop, Spark, Kafka, Flink, Cassandra, and more recently Iceberg, Arrow, and DuckDB form a stack that powers most large analytical workloads. Commercial vendors such as Databricks, Snowflake, Cloudera, and Confluent have layered managed services and proprietary extensions on top of these projects, while cloud hyperscalers integrate them deeply into their platforms. The 2020s also saw the rise of the 'modern data stack' — dbt, Airflow, and reverse-ETL tools — which modularized analytics engineering. Key disputes concern open-source licensing and sustainability. Several foundational projects (Elastic, MongoDB, Redis, HashiCorp) have relicensed to more restrictive terms in 2018–2024 to limit free-rider exploitation by cloud providers, prompting community forks such as OpenSearch and Valkey. International standardization through Apache Software Foundation, Linux Foundation, and CNCF remains influential, but governments increasingly scrutinize foreign contributions to open-source projects on national-security grounds.

Key players

Big Data Mining

Data mining as a discipline coalesced in the 1990s with the KDD conference series and foundational algorithms like Apriori (1994) and k-means clustering. The 2000s brought ensemble methods (random forests, gradient boosting) that remain workhorses for tabular data, while the 2010s saw deep learning subsume many tasks involving images, text, and sequences. Today's pipelines often combine classical mining techniques with foundation models, applied to streams from sensors, transactions, social media, and IoT devices. The contemporary debate is less about technique and more about governance. Predictive mining in domains like credit scoring, hiring, policing, and welfare allocation has produced documented harms — including disparate impact identified in research on the COMPAS recidivism tool and several EU welfare-fraud systems struck down by national courts. The EU AI Act (entering into force in 2024) categorizes many such mining applications as 'high risk,' while the US relies on sectoral rules and the 2023 Executive Order on AI. Cross-border mining for security and commercial purposes also raises persistent tensions over extraterritorial reach.

Key players

Big Data Management and Governance

Data management has evolved from the relational era (Codd, 1970) through data warehousing (Inmon, Kimball, 1990s) to today's lakehouse architectures combining structured and unstructured stores. Modern data management spans cataloging, lineage, quality, master-data management, and lifecycle policies. The DAMA-DMBOK framework and ISO/IEC 38505 provide reference models, while regulations such as GDPR, HIPAA, Basel III data-quality requirements (BCBS 239), and sector-specific rules impose concrete obligations. The central issue is cross-border data governance. The OECD's Data Free Flow with Trust (DFFT) initiative, launched by Japan at the 2019 G20 Osaka summit, seeks to reconcile free flows with protection, but states diverge sharply: the EU emphasizes rights-based protection, the US a market-led approach, China and Russia data localization, and many developing states an emerging 'data sovereignty' stance tied to development goals. Disputes over data brokers, AI training data, and government access requests (e.g., the US CLOUD Act of 2018) keep the management agenda contested.

Key players

Resources

News, lessons, and country profiles to prep for International Conference on Big Data Computer Science and Information (ICBDCSI).

Recent reporting to ground your prep

Arming an archipelago: Is time running out for Europe's oldest DMZ? | Euronews

Comprehensive approach to security: Federal Council launches consultation on Switzerland's security policy strategy

Europe's June Heatwave: Infrastructure Crisis

Ukraine Drone Strikes Crimea Power Grid

Lessons

Bite-sized lessons to build the basics

Dynamic Analysis

The Power-Interest Grid

Hidden Stakeholders

Engagement Strategies

Courses

Guided courses that go deeper on the topic

Crisis Management

Advanced Negotiation

Multilateral Negotiation

Policy Brief Writing

The states in play, with the data that shapes their stance

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Ukraine November 2026

By committee

Frequently asked questions

International Conference on Big Data Computer Science and Information (ICBDCSI)

Nov 12, 2026Kharkiv, UkraineIn-person

Registration open

Committees & topics

Topics
Big Data Techniques, Models and Algorithms · Big Data Infrastructure and Platforms · Big Data Search and Mining · Big Data Security, Privacy and Trust · Big Data Applications in Bioinformatics, Multimedia and Beyond · Big Data Tools and Systems · Big Data Mining · Big Data Management and Governance

Why it matters

How to prepare

Country perspectives

Where the most-relevant 4 countries stand on the core issues in play. Click through for the full country profile.

Ukraine

A host nation with a strong interest in leveraging big data and computer science for national development and security, particularly given its geopolitical context.

Role in topic

United States

A global leader in big data and computer science, with significant contributions to research, development, and policy frameworks.

Role in topic

China

A rapidly advancing power in big data and AI, with a focus on national strategic applications and extensive data collection.

Role in topic

Germany

A proponent of strong data protection and ethical AI, balancing technological advancement with regulatory oversight.

Role in topic