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Data Stream Management

on high-speed streams to loading real-time data feeds into a streaming warehouse for off-line analysis. We will discuss two types;

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Real-time Linked Dataspaces

concepts of real-time linked dataspaces, and establishes the need to evolve data management techniques in order to meet the challenges of enabling;

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Real-time Linked Dataspaces

concepts of real-time linked dataspaces, and establishes the need to evolve data management techniques in order to meet the challenges of enabling;

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Real-Time & Stream Data Management

push new information to clients. Systems for data stream management and processing, on the other hand, are natively push oriented and thus;

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Real-Time Data Analytics for Large Scale Sensor Data

>Provides a focused approach towards the design and implementation of real-time intelligent systems for the management of sensor data in large-scale;

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Stream Ciphers in Modern Real-time IT Systems

volume and stream bit depth of converted data, which makes them the most popular in modern real-time IT systems. In particular, we analyze the;

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Parallel Computing Architectures and APIs

, to extract insights and actionable results in real time. Application domains requiring data stream management include military, homeland;

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Database Systems for Advanced Applications

management; indexing and distributed systems; network embedding; trajectory and time series data processing; data mining; query processing and;

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Database Systems for Advanced Applications

and optimization; search and information retrieval; string and sequence processing; stream date processing; graph and network data processing;

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Mastering Kafka Streams and ksqlDB Building realtime data systems by example

Working with unbounded and fast-moving data streams has historically been difficult. But with Kafka Streams and ksqlDB, building stream;

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Data Streams

, with Data Stream Management Systems (DSMSs) and special hardware to deal with data speeds. Even beyond Computer Science, data stream concerns;

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Scalable Big Data Architecture

Hadoop ecosystem. It focuses on processing long jobs, architecture, stream data patterns, log analysis, and real time analytics. Every;

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Event Streams in Action

near-real time stream data, event logging and aggregation, and data processing into a unified event stream. By efficiently creating a single;

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Kafka Streams - Real-time Stream Processing

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Fundamentals Of Stream Processing

, heterogeneous, continuous data streams, to extract insights and actionable results in real time. This comprehensive, hands-on guide combining the;

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Handbook of Big Data Analytics: Applications in ICT, security and business analytics

database management, processing frameworks and architectures, data lakes, query optimization strategies, towards real-time data processing, data;

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Handbook of Big Data Analytics: Methodologies

database management, processing frameworks and architectures, data lakes, query optimization strategies, towards real-time data processing, data;

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Mapping the Total Value Stream

Management for the Lean Office. It reinforces fundamental concepts and theoretical models with real-world applications and complete examples of the;

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Mapping the Total Value Stream

Management for the Lean Office. It reinforces fundamental concepts and theoretical models with real-world applications and complete examples of the;

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Anomaly Detection and Complex Event Processing Over IoT Data Streams

Anomaly Detection and Complex Event Processing over IoT Data Streams: With Application to eHealth and Patient Data Monitoring presents;

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Real-Time Big Data Analytics

Design, process, and analyze large sets of complex data in real time About This Book * Get acquainted with transformations and;

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Stream Hydrology

emphasis on applications, from collecting and analysing field measurements to using data and tools in stream management. It is updated to include;

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Stream Hydrology An Intro For Ecologist

on applications, from collecting and analysing field measurements to using data and tools in stream management.* Updated to include new;

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Mastering Apache Storm

Master the intricacies of Apache Storm and develop real-time stream processing applications with ease About This Book * Exploit the;

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Database Systems for Advanced Applications

, privacy and trust, keyword search, data stream management and data quality.;

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Spark in Action

. Apache Spark is a big data processing framework perfect for analyzing near-real-time streams and discovering historical patterns in batched;

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Enabling Real-Time Business Intelligence

the workshop. The topics cover all stages of the business intelligence cycle, including capturing of real-time data, handling of temporal or;

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