تعریف Big Data. موضوعات مطرح در حوزه : Big Data. 1. Big Data Foundations

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1 بو نام خدا گسارش مطالعو برخی مفاىیم زىره رضایی کینجی تعریف Big Data Big Data کلکغیو ی اص هجووػ داد ای بغیاس بضسگ و پیچیذ اعت ک ایي اهش هوجب هی شود پشداصػ آى با اعتفاد اص عیغتن ای هذیشیت پایگا داد و یا بش اه ای کاسبشدی پشداصػ داد ع تی بغیاس عخت صوست گیشد. پشداصػ ایی هثل رخیش جغتجو اشتشاک گزاسی ا تقال تجضی و تحلیل و... اص ایي قبیل ا ذ. ا ذاص فقظ یکی اص هجووػ داد ای هوجود دس Big Data هی توا ذ د ا تشابایت یا بیشتش باشذ. ب ػ واى تؼشیفی دیگش هی تواى گفت Big Data ب حجن باالیی اص داد ای عاختاسی و غیش عاختاسی گفت هی شود ک بغیاس بضسگ بود و پشداصػ آى با پایگا داد و تک یک ای شم افضاسی ع تی بغیاس دشواس اعت. موضوعات مطرح در حوزه : Big Data 1. Big Data Foundations a. Novel Theoretical Models for Big Data b. New Computational Models for Big Data c. Data and Information Quality for Big Data d. New Data Standards 2. Big Data Infrastructure a. Cloud/Grid/Stream Computing for Big Data b. High Performance/Parallel Computing Platforms for Big Data c. Autonomic Computing and Cyber infrastructure, System Architectures, Design and Deployment d. Energy efficient Computing for Big Data e. Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data f. Software Techniques and Architectures in Cloud/Grid/Stream Computing g. Big Data Open Platforms h. New Programming Models for Big Data beyond Hadoop/MapReduce, STORM i. Software Systems to Support Big Data Computing 1

2 3. Big Data Management a. Advanced database and Web Applications b. Novel Data Model and Databases for Emerging Hardware c. Data Preservation d. Data Provenance e. Interfaces to Database Systems and Analytics Software Systems f. Data Protection, Integrity and Privacy Standards and Policies g. Information Integration and Heterogeneous and Multi-structured Data Integration h. Data management for Mobile and Pervasive Computing i. Data Management in the Social Web j. Crowdsourcing k. Spatiotemporal and Stream Data Management l. Scientific Data Management m. Workflow Optimization n. Database Management Challenges: Architecture, Storage, User Interfaces 4. Big Data Search and Mining a. Social Web Search and Mining b. Web Search c. Algorithms and Systems for Big Data Search d. Distributed, and Peer-to-peer Search e. Big Data Search Architectures, Scalability and Efficiency f. Data Acquisition, Integration, Cleaning, and Best Practices g. Visualization Analytics for Big Data h. Computational Modeling and Data Integration i. Large scale Recommendation Systems and Social Media Systems j. Cloud/Grid/Stream Data Mining Big Velocity Data k. Link and Graph Mining l. Semantic based Data Mining and Data Preprocessing m. Mobility and Big Data n. Multimedia and Multi-structured Data Big Variety Data 5. Big Data Security & Privacy a. Intrusion Detection for Gigabit Networks b. Anomaly and APT Detection in Very Large Scale Systems c. High Performance Cryptography 2

3 d. Visualizing Large Scale Security Data e. Threat Detection using Big Data Analytics f. Privacy Threats of Big Data g. Privacy Preserving Big Data Collection/Analytics h. HCI Challenges for Big Data Security & Privacy i. User Studies for any of the above j. Sociological Aspects of Big Data Privacy 6. Big Data Applications a. Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication b. Big Data Analytics in Small Business Enterprises (SMEs), c. Big Data Analytics in Government, Public Sector and Society in General d. Real-life Case Studies of Value Creation through Big Data Analytics e. Big Data as a Service f. Big Data Industry Standards g. Experiences with Big Data Project Deployments (IEEE Big Data 2013) 3

4 تعریف Model transformation قبل اص آ ک بخوا ین Model transformation سا تؼشیف ک ین بایذ بذا ین Model driven engineering چیغت. Model driven engineering یک سوػ توعؼ شم افضاس اعت ک ب جای الگوسیتن و هحاعب سوی عاخت و ب کاسگیشی هذل ا تاکیذ داسد.ایي بذاى ه ظوس اعت ک ب جای تولیذ کذ ب هذل توج هی ک ذ و پظ اص اسای یک هذل ه اعب )بشای هثال دس قالب کالط دیاگشام( با اعتفاد اص ابضاس ایی ایي هذل سا ب کذ تبذیل هی ک ذ. Model transformation دس Model driven engineering سوشی بشای تضویي عاصگاسی خا واد ای اص هذل اعت ک هی تواى ب آى ب صوست یک بش اه گا کشد ک وسودی آى یک هذل اعت و دس صوست پزیشػ آى هذل هذل دیگشی ب ػ واى خشوجی اسای هی د ذ. دس Model transformation اص صباى ایی اعتفاد هی شود ک ب آى ا صباى ای Model transformation هی گوی ذ.اص ایي صباى ا هی تواى ب UML-RSDS اشاس کشد ک اص UML و OCL اعتفاد هی ک ذ. موضوعات مطرح در حوزه : Model transformation Transformation paradigms and languages: graph rewriting, tree rewriting, attribute grammars rule based, declarative, imperative, and functional textual, graphical pattern matching transformation by example/demonstration modularity, reusability, and composition comparison of transformation languages theoretical foundations Transformation algorithms and strategies: bidirectional transformation incremental transformation scalability and optimization termination and confluence higher order transformation transformation chains Development of transformations: 4

5 specification, verification, and validation testing and debugging evolution development processes tool support benchmarking of transformation engines Applications and case studies: refactoring aspect weaving model comparison, differencing, and merging model synchronization and change propagation coevolution of models, meta-models, and transformations roundtrip/reverse/forward engineering industrial experience reports empirical studies (model-transformation.org) 5

6 تعریف Active data warehouse Data warehouse یک وع پایگا داد خیلی بضسگ اعت ک بشای گضاسػ و تجضی و تحلیل داد ا هوسد اعتفاد قشاس هی گیشد. Active data warehouse وػی data warehouse اعت ک طوسی پیاد عاصی هی شود ک بشای سویذاد ای پیشاهذ ب صوست real-time تصوین گیشی هی ک ذ. با توج ب ایي ویژگی صهاى پاعخ ایي وع اص data warehouse عشیغ هی باشذ.یک Active data warehouse ب صوست آ الیي تاص هی شود و باالتشیي عاصگاسی سا بشای اطالػات رخیش شذ و داد ای ب سوص شذ ب دعت هی آوسد. موضوعات مطرح در حوزه Active data warehouse Data warehousing foundations and architectures Data warehouse Modeling and Design Maintenance and evolution of data warehouses Software Engineering techniques for DW and OLAP Security, personalization and privacy in data warehouses Data extraction, cleaning, and loading(etl) Active/Real-Time data warehouses Multidimensional modeling and queries Physical organization of data warehouses Performance optimization and tuning Data warehousing with unstructured data (e.g., text) and semi-structured data (e.g., XML) Multimedia data warehouses Data warehouses in Scientific Applications Spatial, temporal, and spatio-temporal data warehouses Integration of OLAP and information retrieval/search engines Integration of data warehouses/olap and data mining Warehousing stream and sensor data OLAP on Documents -Keyword search in Data Warehouses Workflows in Data warehouses Semantic Web & Deep Web in Data warehouses Using MapReduce in Data warehouses SQL Vs MapReduce for Analytical Processing DW Deployment (Parallel machine, Database Clusters, Cloud, etc.) Data warehousing and OLAP for Big Data Smart Grid & DW 6

7 )ACM Sixteenth International Workshop On Data Warehousing and OLAP (DOLAP 2013)( 7