Large-scale computing services revolve around the management, distribution, and analysis of massive data sets. For over 40 years, Berkeley has led the world in recognizing and advancing the centrality of data in computing. Faculty and students at Berkeley have repeatedly defined and redefined the broad field of data management, combining deep intellectual impact with the birth of multi-billion dollar industries, including relational databases, RAID storage, scalable Internet search, and big data analytics. Berkeley also gave birth to many of the most widely-used open source systems in the field including INGRES, Postgres, BerkeleyDB, and Apache Spark. Today, our research continues to push the boundaries of data-centric computing, taking the foundations of data management to a broad array of emerging scenarios.
Declarative languages and runtime systems
Design and implementation of declarative programming languages with applications to distributed systems, networking, machine learning, metadata management, and interactive visualization; design of query interface for applications.
Scalable data analysis and query processing
Scalable data processing in new settings, including interactive exploration, metadata management, cloud and serverless environments, and machine learning; query processing on compressed, semi-structured, and streaming data; query processing with additional constraints, including fairness, resource utilization, and cost.
Consistency, concurrency, coordination and reliability
Coordination avoidance, consistency and monotonicity analysis; transaction isolation levels and protocols; distributed analytics and data management, geo-replication; fault tolerance and fault injection.
Data storage and physical design
Hot and cold storage; immutable data structures; indexing and data skipping; versioning; new data types; implications of hardware evolution.
Metadata management
Data lineage and versioning; usage tracking and collective intelligence; scalability of metadata management services; metadata representations; reproducibility and debugging of data pipelines.
Systems for machine learning and model management
Distributed machine learning and graph analytics; physical and logical optimization of machine learning pipelines; online model management and maintenance; prediction serving; real-time personalization; latency-accuracy tradeoffs and edge computing for large-scale models; machine learning lifecycle management.
Data cleaning, data transformation, and crowdsourcing
Human-data interaction including interactive transformation, query authoring, and crowdsourcing; machine learning for data cleaning; statistical properties of data cleaning pipelines; end-to-end systems for crowdsourcing.
Interactive data exploration and visualization
Interactive querying and direct manipulation; scalable spreadsheets and data visualization; languages and interfaces for interactive exploration; progressive query visualization; predictive interaction.
Secure data processing
Data processing under hom*omorphic encryption; data compression and encryption; differential privacy; oblivious data processing; databases in secure hardware enclaves.
Foundations of data management
Optimal trade-offs between storage, quality, latency, and cost, with applications to crowdsourcing, distributed data management, stream data processing, version management; expressiveness, complexity, and completeness of data representations, query languages, and query processing; query processing with fairness constraints.
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Regarding the concepts mentioned in the article you provided, I can provide information on the following topics:
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Large-scale computing services: These services revolve around the management, distribution, and analysis of massive data sets. They are essential for various industries and applications, including cloud computing, big data analytics, and scalable internet search.
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Data management: Data management involves the organization, storage, retrieval, and manipulation of data. It encompasses various aspects such as data storage and physical design, metadata management, and data cleaning and transformation.
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Declarative languages and runtime systems: Declarative programming languages focus on specifying what needs to be done rather than how to do it. They are used in various domains, including distributed systems, networking, machine learning, metadata management, and interactive visualization.
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Scalable data analysis and query processing: This involves developing techniques and systems for processing and analyzing large-scale data sets efficiently. It includes interactive exploration, metadata management, cloud and serverless environments, machine learning, and query processing on compressed, semi-structured, and streaming data.
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Consistency, concurrency, coordination, and reliability: These concepts are crucial for ensuring the correctness and reliability of distributed systems. They involve coordination avoidance, consistency analysis, transaction isolation levels and protocols, fault tolerance, and geo-replication.
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Systems for machine learning and model management: This area focuses on developing distributed systems and optimization techniques for machine learning and graph analytics. It includes physical and logical optimization of machine learning pipelines, online model management, prediction serving, real-time personalization, and machine learning lifecycle management.
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Interactive data exploration and visualization: This involves developing tools and techniques for interactive querying, direct manipulation, scalable spreadsheets, data visualization, and predictive interaction.
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Secure data processing: This area focuses on ensuring the security and privacy of data during processing. It includes techniques such as data processing under hom*omorphic encryption, data compression and encryption, differential privacy, and databases in secure hardware enclaves.
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Foundations of data management: This area explores fundamental trade-offs between storage, quality, latency, and cost in data management systems. It includes topics such as expressiveness and complexity of data representations, query languages, and query processing, as well as query processing with fairness constraints.
These are just brief descriptions of the concepts mentioned in the article. If you have any specific questions or would like more detailed information on any of these topics, feel free to ask!
