The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Dynamic Pipeline Scheduling for Improving Interactive Query Performance
Proceedings of the 27th International Conference on Very Large Data Bases
A Model-Based, Open Architecture for Mobile, Spatially Aware Applications
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Issues in data stream management
ACM SIGMOD Record
Chain: operator scheduling for memory minimization in data stream systems
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Dynamic plan migration for continuous queries over data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
PIPES: a public infrastructure for processing and exploring streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Load management and high availability in the Medusa distributed stream processing system
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Benefits of Integrating Meta Data into a Context Model
PERCOMW '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops
StreamGlobe: adaptive query processing and optimization in streaming P2P environments
DMSN '04 Proceeedings of the 1st international workshop on Data management for sensor networks: in conjunction with VLDB 2004
Adaptive Visualization Pipeline Decomposition and Mapping onto Computer Networks
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Distributed operation in the Borealis stream processing engine
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
StreamGlobe: processing and sharing data streams in grid-based P2P infrastructures
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Computation on Programmable Graphics Hardware
IEEE Computer Graphics and Applications
Dealing with Overload in Distributed Stream Processing Systems
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Performance evaluation of JXTA communication layers
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid - Volume 01
HybMig: A Hybrid Approach to Dynamic Plan Migration for Continuous Queries
IEEE Transactions on Knowledge and Data Engineering
SPC: a distributed, scalable platform for data mining
Proceedings of the 4th international workshop on Data mining standards, services and platforms
Operator scheduling in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
SPADE: the system s declarative stream processing engine
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Tool support for the design and management of context models
Information Systems
Usability analysis of compression algorithms for position data streams
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Techniques for efficient and distributed processing of huge, unbound data streams have made some impact in the database community. Sensors and data sources, such as position data of moving objects, continuously produce data that is consumed, e.g., by location-aware applications. Depending on the domain of interest, e.g. visualization, the processing of such data often depends on domain-specific functionality. This functionality is specified in terms of dedicated operators that may require specialized hardware, e.g. GPUs. This creates a strong dependency which a data stream processing system must consider when deploying such operators. Many data stream processing systems have been presented so far. However, these systems assume homogeneous computing nodes, do not consider operator deployment constraints, and are not designed to address domain-specific needs. In this paper, we identify necessary features that a flexible and extensible middleware for distributed stream processing of context data must satisfy. We present NexusDS, our approach to achieve these requirements. In NexusDS, data processing is specified by orchestrating data flow graphs, which are modeled as processing pipelines of predefined and general operators as well as custom-built and domain-specific ones. We focus on easy extensibility and support for domain-specific operators and services that may even utilize specific hardware available on dedicated computing nodes.