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2026 8th International Conference on Big Data Management https://www.icbdm.org/index.htmlWelcome to ICBDM 2026! 2026 8th International Conference on Big Data Management (ICBDM 2026) will be held in Derby, UK during June 24-26, 2026! It's hosted by University of Derby. The theme of this year is "Data Management in Statistics and Data Science".The conference aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of big data management. It also provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of big data management.Important DatesSubmission Deadline: April 5, 2026Acceptance Notification: May 5, 2026Registration Deadline: May 20, 2026Camera-Ready Version: May 20, 2026Early Bird Registration End: May 20, 2026JournalA few selected papers with extended version will be published in theJournal of Advanced Management ScienceISSN: 2810-9740 Frequency: SemiannuallyDOI: 10.18178/joamsAbstracting/ Indexing: CNKI, Google Scholar, CrossrefTemplate: JOAMS TemplateCall for PapersTopics of interest for submission include, but are not limited to:Track 1: Big Data Analysis and ManagementData Acquisition, Integration, Cleaning, and Best PracticesBig Data Search Architectures, Scalability and EfficiencyCloud/Grid/Stream Data Mining- Big Velocity DataSemantic-based Data Mining and Data Pre-processingBig Data as a ServiceData Lifecycle Management: From Collection to ArchivingData Governance Frameworks and Best PracticesData Management Standards (e.g., FAIR principles: Findable, Accessible, Interoperable, Reusable)Ethical Considerations in Data ManagementAlgorithms and Systems for Big Data SearchVisualization Analytics for Big DataChallenges in Managing Large-scale DatasetsBig Data Processing Frameworks (e.g., Apache Spark, Apache Flink)Scalable Storage Solutions for Big DataMobility and Big DataMethods for Data Collection: Surveys, Experiments, Sensors, Web ScrapingData Integration Techniques: ETL (Extract, Transform, Load) ProcessesSearch and Mining of Variety of Data including Scientific and Engineering, Social, Sensor/IoT/IoE, and Multimedia DataTrack 2: Data Structures and Data ModelsMultimedia and Multi-structured Data- Big Variety DataComputational Modeling and Data IntegrationRelational Databases (e.g., SQL) vs. NoSQL Databases (e.g., MongoDB, Cassandra)Data Warehousing and Data Lake ArchitecturesCloud-based Data Storage Solutions (e.g., AWS S3, Google BigQuery)Distributed Storage Systems for Big Data (e.g., Hadoop HDFS)Data Quality Metrics: Accuracy, Completeness, Consistency, and TimelinessTechniques for Data Cleaning and PreprocessingHandling Missing Data: Imputation Methods and StrategiesOutlier Detection and Treatment in DatasetsReal-Time Data Collection and Streaming Data ManagementImportance of Metadata in Data ManagementMetadata Standards and Schemas (E.G., Dublin Core, Schema.Org)Tools for Metadata Extraction and ManagementRole of Metadata in Data Discovery and ReuseVisualization of High-Dimensional DataManaging Unstructured Data (E.G., Text, Images, Videos)Data Silos and Interoperability IssuesTrack 3: Big Data Security and PrivacyVisualizing Large Scale Security DataThreat Detection using Big Data AnalyticsPrivacy Threats of Big DataPrivacy Preserving Big Data Collection/AnalyticsHCI Challenges for Big Data Security & PrivacySociological Aspects of Big Data PrivacyTrust Management in IoT and Other Big Data SystemsData Encryption and Anonymization TechniquesRole-based Access Control (RBAC) and Data PermissionsCompliance with Data Protection Regulations (e.g., GDPR, CCPA)Secure Data Sharing and Transfer ProtocolsVisualizing Large Scale Security DataBalancing Data Accessibility with SecurityTrust Management in IoT and Other Big Data SystemsHCI Challenges for Big Data Security & PrivacyTrack 4: Big Data Analysis Tools and Key TechnologiesHealthcare: Managing Electronic Health Records (EHR) and Patient DataFinance: Data Management for Fraud Detection and Risk AnalysisEnvironmental Science: Managing Climate and Satellite DataSocial Sciences: Handling Survey and Census DataE-Commerce: Customer Data Management and PersonalizationComplex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, TelecommunicationBig Data Analytics in Small Business Enterprises (SMEs)Big Data Analytics in Government, Public Sector and Society in GeneralReal-Life Case Studies of Value Creation through Big Data AnalyticsExperiences with Big Data Project DeploymentsBig Data as a ServiceBig Data Industry StandardsTrack 5: Application of Big Data in Information SystemsTools and Techniques for Exploratory Data Analysis (EDA)Interactive Dashboards for Data Exploration (E.G., Tableau, Power BI)Open-Source Data Management Tools (E.G., Apache Nifi, Talend)Data Management Platforms (E.G., Snowflake, Databricks)Cloud-Native Data Management SolutionsAutomation Tools for Data Pipelines (E.G., Airflow, Prefect)Data Pipelines for Machine Learning WorkflowsFeature Engineering and Dataset PreparationManaging Labeled and Unlabeled Data for Supervised and Unsupervised LearningData Versioning and Reproducibility in ML ExperimentsData Management for AI and Deep LearningBlockchain for Secure and Decentralized Data ManagementFederated Learning and Privacy-Preserving Data ManagementQuantum Computing and Its Impact on Data Management

2026 the 11th International Conference on Big Data Analytics (ICBDA)April 11-14, 2026 @ Waseda University, Tokyo, JapanLink: https://www.icbda.orgThe realm of Big Data analytics has recently captured significant attention across diverse research communities including computer science, information technology, and social sciences. The term "Big Data" has taken on widespread significance, reshaping realms such as science, engineering, medicine, healthcare, finance, business, and even society at large. In this context, the 11th International Conference on Big Data Analytics (ICBDA2026), serves as a premier platform for disseminating cutting-edge research findings in the field of Big Data Research, Development, and Application.ICBDA 2026 invites contributions from a broad spectrum of domains, encompassing, but not restricted to, Big Data Models and Algorithms, Big Data Architectures, Big Data Search and Mining, healthcare, social networks, and media applications. Authors are encouraged to submit papers covering a diverse array of big data topics, such as service-oriented technologies, machine learning, predictive analytics, data modeling, system architectures, data mining, and simulation. Critical DatesFull-paper Submission Deadline December 20th, 2025 Abstract Submission Deadline December 20th, 2025 Notification Deadline January 20th, 2026 Registration Deadline February 10th, 2026 Camera Ready Date February 20th, 2026 Conference Dates April 11-14, 2026 Paper ReviewAll papers will undergo a thorough peer review process. Accepted full papers that are presented at the conference will be published in proceedings. Publishing through IEEE provides authors with a reputable, global platform on which to share their research with an audience which includes corporate researchers, academia and government.Conference ProceedingsAfter a careful reviewing process, all accepted papers after proper registration and presentation, will be published in the ICBDA 2026 conference Proceedings.Topics of InterestTopics of interest include, but are not limited to:Big Data Models and AlgorithmsFoundational Models for Big DataAlgorithms and Programming Techniques for Big Data ProcessingBig Data Analytics and MetricsRepresentation Formats for Multimedia Big DataBig Data ArchitecturesCloud Computing Techniques for Big DataBig Data as a ServiceBig Data Open PlatformsBig Data in Mobile and Pervasive ComputingBig Data ManagementBig Data Persistence and PreservationBig Data Quality and Provenance ControlManagement Issues of Social Network Big DataBig Data Protection, Integrity and PrivacyModels and Languages for Big Data ProtectionPrivacy Preserving Big Data AnalyticsBig Data EncryptionSecurity Applications of Big DataAnomaly Detection in Very Large Scale SystemsCollaborative Threat Detection using Big Data AnalyticsBig Data Search and MiningAlgorithms and Systems for Big Data SearchDistributed, and Peer-to-peer SearchMachine learning based on Big DataVisualization Analytics for Big DataBig Data for Enterprise, Government and SocietyBig Data EconomicsReal-life Case Studies of Value Creation through Big Data AnalyticsBig Data for Business Model InnovationBig Data ToolkitsBig Data in Business Performance ManagementSME-centric Big Data AnalyticsBig Data for Vertical Industries (including Government, Healthcare, and Environment)Scientific Applications of Big DataLarge-scale Social Media and Recommendation SystemsExperiences with Big Data Project DeploymentsBig Data in Enterprise Management Models and PracticesBig Data in Government Management Models and PracticesBig Data in Smart Planet SolutionsBig Data for Enterprise TransformationBlockchainBlockchain based lightweight data structures for IoT dataBlockchain based IoT security solutionsBlockchain in cyber physical systemsBlockchain in social networkingBolckchain in crowdsourcing and crowdsensingBlockchain in 5GBlockchain in edge and cloud computingBlockchain and trust management

SN Computer SciencePublication type: Journals (Q2)ISSN: 26618907, 2662995XH-Index: 49Publication Fee: £2290.00 GBP / $3190.00 USD / €2590.00 EUR.Link: https://www.scimagojr.com/journalsearch.php?q=21101083109&tip=sid&clean=0SPRINGER NATURE Link: https://link.springer.com/journal/42979?IFASN Computer Science is a broad-based, hybrid, peer reviewed journal that publishes original research in all the disciplines of computer science including various inter-disciplinary aspects. The journal aims to be a global forum of, for, and by the community and offers:Rapid peer review under the expert guidance of a global Editorial BoardNo color or page charges and free submissionHigh visibilityOpportunities to Societies/Conferences/Institutes/Laboratories/Corporates to ‘partner’ with the Journal and enjoy the benefit of planning and publishing issues in hot areas of research, without being under the pressure of publishing a full-fledge journalSN Computer Science welcomes submissions from a wide range of topics including, but not limited to:Artificial IntelligenceRoboticsMachine learningComputer VisionPattern RecognitionImage ProcessingComputer GraphicsHuman-Computer InterfaceDocument and Handwriting ProcessingVideo TechnologiesBiometrics and Computer ForensicsSoft ComputingBrain ComputingQuantum ComputingInformation RetrievalInternet Computing and Data MiningTheoretical Computer Science: Logic, Algorithms, and ComplexityAutomated Proofs and Formal VerificationComputational GeometryInformation TheorySpeech and Signal ProcessingAlgorithms and Data StructuresProgramming LanguagesSoftware EngineeringComputer ArchitectureComputer and Communication NetworksComputer and Network PerformanceModeling and SimulationEnergy Consumption and Harvesting Computers & NetworksGreen ICTComputer and Network Security/PrivacyCryptographyHigh Performance ComputingParallel Computing and ArchitectureDistributed and Cloud ComputingSocial NetworksDatabase Systems and TheoryComputers and Networks in Supply Chains and ManufacturingComputers and Networks for Health SystemsComputational Biology and BioinformaticsCyber-Physical SystemsInternet of Things (IoT)Mathematical Programming and Combinatorial OptimizationEconomics and ComputationSN Computer Science publishes papers in the following categories: Original Research, as well as relevant hardware, and/or software architectures, Survey and Review Articles. All papers are evaluated on the basis of scientific content. Submissions are first screened for research and publication ethics prior to peer review. The journal reviews each submission from a sound science perspective.SN Computer Science is an online-only journal and its print ISSN (2662-995X) is ceased.

Application deadline: July 4th, 2024Location: Nova Information Management School Universidade Nova de Lisboa Campus de Campolide Lisbon, PortugalLink: https://academicpositions.com/ad/nova-ims/2024/faculty-positions-for-assistant-professor-businesss-intelligence-and-analytics/218536The PositionNOVA Information Management School (NOVA IMS) at Universidade Nova de Lisboa invites for an expression of interest for a position as a Professor (at Assistant/ Associate or Full Professor level) in Business Intelligence and Data Warehouses.The position is to be filled in NOVA IMS’ facilities in Lisbon, Portugal.Salary and conditions are competitive and will be commensurate with qualifications and experience.QualificationsNOVA IMS seeks a candidate with a strong commitment to excellence in scholarly research and teaching. He/ she will be expected to teach in undergraduate as well as graduate programme classes related to Business Intelligence and Databases and one or more of the following areas of expertise:Business IntelligenceDatabase Management SystemData WarehousingData AnalyticsThe successful applicants should have a strong academic background and an international profile in the aforementioned expertise areas with a record of research publications in top-ranked field journals. Moreover, relevant experience in obtaining funding and managing national and international research projects will be considered an advantage.The successful candidate should be able to provide strong and committed leadership in education, including curriculum development, course coordination and educational innovation, the preparation and delivery of lectures, seminars and tutorials, consultation with students, and marking and assessment.The main requirements for applicants are listed below:PhD or doctoral degree from a recognized university;Excellent scientific achievements, documented by internationally recognized publications;Excellent didactic skills;Demonstrate an excellent teaching ability at both the undergraduate and graduate levels;High capacity to develop pedagogical innovations and to manage a programme;Excellent communication skills (oral, written, presentation);Proficient in English (oral and written);Ability to teach in English;Collaborative skills, initiative, result oriented, organization, and capacity to work in an interdisciplinary environment.The SchoolNOVA Information Management School (NOVA IMS, www.novaims.unl.pt) is the School of Information Management and Data Science of Universidade Nova de Lisboa. It is one of the 9 academic units of Universidade NOVA de Lisboa, a university institution with internationally recognized research and quality teaching.It is dedicated to converting data into value, under the motto “Data with Purpose”, through a wide range of activities, namely teaching, and research and development activities that are largely supported by international partnerships, offering a unique research environment to address its main research challenges.The quality of its training is recognized globally, through different national and international accreditations, and outstanding positions achieved in various fields, namely in the Eduniversal ranking (seven of its masters and postgraduate degrees are recognized among the best in the world in their respective areas).NOVA IMS has more than 30 years of accumulated experience in data processing and analysis, which it now puts at the service of digital transformation and the “Big Data” environments in which we live in, namely through a wide range of laboratories (NOVA ANALYTICS LABS powered by NOVA IMS). Today, it has more than 3.000 students from 81 different countries, and high levels of internationalization in all the activities it carries out from Lisbon to the world.How to applyInterested candidates are invited to send the application material listed below by email to rh@novaims.unl.pt (mentioning the reference of the application):A cover letter including motivation to join NOVA IMS;A complete curriculum vitae with a list of publications and a list of classes taught;A copy of the three most significant recent publications, communications or working papers.Reference: NOVAIMS_ BusinessIntelligenceandAnalytics