Meetings/Workshops on Information & Knowledge Management, Big Data Computing in the United States (USA)

About Apache Storm Training: Apache Storm is an open-source and distributed stream processing computation framework used for processing large volumes of high-velocity data.and its comparison with hadoop, Big Data world., etc.This Apache StormTraining from MindMajix will give you a working knowledge of the open-source Storm computational engine. You will be able to do distributed real-time data processing and come up with valuable insights.

Mindmajix Technologies INC;     Phone: [9174568403];     Email: info@mindmajix.com

ApacheStromTraining, Apachestormonline, stormtechnology

Algorithms and Open Source Development,    Training in Computer Science, Courses

American Statistical Association

The American Statistical Association and Interface Foundation of North America invite you to join us at the second annual Symposium on Data Science and Statistics in Bellevue, Washington, May 29 - June 1, 2019. The ASA’s newest conference, the Symposium on Data Science & Statistics, is designed for data scientists, computer scientists, and statisticians analyzing and visualizing complex data. SDSS provides a unique opportunity for data scientists, computer scientists, and statisticians to come together and exchange ideas.

Alteryx, Inc.

You’ve disrupted the status quo, dissolved data conventions and altered everything we knew about analytics. This year, it’s time to put your groundbreaking insights on the main stage at our annual user conference. Join us in Nashville as we celebrate the game changing stories that educate leaders and motivate a community of data experts to shatter more barriers than ever before. It’s all about You.Amplified.

ALTERYX PLATFORM, data analytics

Association for Computing Machinery (ACM)

The annual KDD conference is the premier interdisciplinary conference bringing together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.

The annual VLDB conference is a premier annual international forum for database researchers, vendors, practitioners, application developers, and users. PVLDB, established in 2008, is a scholarly journal for short and timely research papers, with a journal-style review and quality assurance process. PVLDB is distinguished by a monthly submission process with rapid reviews. Its issues are published regularly throughout the year. Your paper will appear in PVLDB soon after acceptance, and possibly in advance of the VLDB conference. All papers accepted in time will be published in PVLDB Vol. 12 and also presented at the VLDB 2019 conference.

Email: vldb2019info@gmail.com

Access Methods, Concurrency Control, Recovery, Transactions, Indexing and Search, In-memory Data Management, Hardware Accelerators, Query Processing and Optimization, Storage Management, Privacy and Security in Data Management, Graph Data Management, Social Networks, Recommendation Systems, Data Mining and Analytics, Warehousing, Crowdsourcing, Embedded and Mobile Databases, Real-time Databases, Sensors and IoT, Stream Databases, Data Models and Query Languages, Schema Management and Design, Database Usability, User Interfaces and Visualization, Tuning, Benchmarking, Performance Measurement, Database Administration and Manageability, Distributed Database Systems, Cloud Data Management, NoSQL, Scalable Analytics, Distributed Transactions, Consistency, P2P and Networked Data Management, Database-as-a-Service, Content Delivery Networks, Provenance and Workflows, Spatial, Temporal, and Multimedia Databases, Scientific and Medical Data Management, Profile-based or Context-Aware Data Management, Data Cleaning, Information Filtering and Dissemination, Information Integration, Metadata Management, Data Discovery, Web Data Management, Semantic Web, Heterogeneous and Federated Database Systems, Fuzzy, Probabilistic and Approximate Databases, Information Retrieval, Text in Databases

The ADT 2019 conference focus is on algorithmic decision theory broadly defined, seeking to bring together researchers and practitioners coming from diverse areas of Computer Science, Economics and Operations Research in order to improve the theory and practice of modern decision support.

Algorithms, Argumentation Theory, Artificial Intelligence, Computational Social Choice, Database Systems, Decision Analysis, Discrete Mathematics, Game Theory, Machine Learning, Matching, Multi-agent Systems, Multiple Criteria Decision Aiding, Networks, Optimization, Risk Management, and Utility Theory

Applied Maths: Operational research,    Applied Maths: Neural Networks and Artificial Intelligence, Machine Learning