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- Data for: AFCL: An Abstract Function Choreography Language for Serverless Workflow SpecificationThis data is generated as a result of executing three different workflows of serverless functions on three platfroms: AWS Step Functions, IBM Composer and using our novel AFCL language.
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- Data for: Characterization of Mobile-Device Tasks by their associated Cognitive Load through EEG Data Processing Nº of Users: 26 Description: Participants performed 12 tasks considered in the HuSBIT-10 taxonomy. EEG activity was recorded during each tasks for a 10-second interval (EEG segment)
- Dataset
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- Data for: Soft Errors Detection and Automatic Recovery based on Replication combined with different Levels of CheckpointingComplete list of scenarios that contemplate all the cases of faults that can occur during the execution of the target application, and their consequences
- Dataset
- Document
- Data for: Human Satisfaction as the Ultimate Goal in Ridesharing1. Factors that affect human satisfaction 2. A comparison between the simsat algorithm and the optimal cost in terms of satisfaction 3. The raw data collected from MTurk users 4. Data from Mturk users that was used to learn the satisfaction function
- Tabular Data
- Dataset
- Document
- Data for: Mining the Frequency of Time-constrained Serial Episodes over Massive Data Sequences and StreamsThe dataset contains a pair of sequential data, including both real-world and synthetic ones. The real-world dataset contains telecommunication alarms from a province of China within a particular period. It is provided purely for research, by a famous ISP in China. Notably, for commercial reasons, we can only provide part of the dataset that has been used in our original paper.
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- Data for: Dynamic Virtual Machine Consolidation using a multi-agent system to optimise energy efficiency in Cloud ComputingThis dataset covers five data components of interest developed during our research which is published in “Dynamic Virtual Machine Consolidation using a multi-agent system to optimise energy efficiency in Cloud Computing”. They are: a patch file, peersim-1.0.5-nmcd.patch, for PeerSim version 1.0.5; four zip files contains the source code for the Gossip Contracts framework, GossipContracts-src.zip, the DVMC models, DVMC-src.zip, the module to process the Google dataset, GoogleTraceData-src.zip, and some utility methods, Util-src.zip; and the experiment results files, ResearchData-Results.zip.
- Other
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- Data for: IPsec for High Speed Network Links: Performance Analysis and EnhancementsThis dataset comprise of experimental results for "IPsec for High speed Network Links: Performance Analysis and Enhancements".
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- Document
- Data for: Accelerated CPU-GPUs Implementations for Quaternion Polar Harmonic Transform of Color ImagesThe source code of the parallel quaternion polar harmonic transform moment implementations on multi-core CPU and multi-GPU.
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- Data for: A Location-Based Orientation-Aware Recommender System Using IoT Smart Devices and Social Networks In this study, 20 recommendation lists are provided for each of 7,214 user's locations in total. Four different buffer sizes (two, five, ten, and 15 km) and four UPDs are considered for each user's location to prepare the event recommendation lists. In the implemented scenario, three different scenarios were applied in order to support research claims. The word "SC" refers to a specific scenario as "SC_1" refers to the first scenario. Totally, considering four circle buffers and four UPDs in each buffer, 16 buffer lines will be prepared as "sc_1_line_1" refers to the first UPD in the buffer size of two kilometers. In addition, "sc_1_cycle" refers to all the event venues that are located in the circle buffer size of two kilometers. "EventVenues" and "venuesMBBs" contain geospatial information for the selected venues and also their extracted minimum bounding boxes. All the imported building located in the city of Calgary, Alberta is in the "calgaryBuilding" table, and "userLocations" shows the spatial information considered for the 7,214 user's locations.
- Tabular Data
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- Data for: Embedding Individualized Machine Learning Prediction Models for Energy Efficient VM Consolidation within Cloud Data CentersThe cross validation results for the prediction models.
- Tabular Data
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