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X-ORIGINAL-URL:https://isg.ics.uci.edu
X-WR-CALDESC:Events for Information Systems Group
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DTSTART:20231105T090000
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230113T123000
DTEND;TZID=America/Los_Angeles:20230113T140000
DTSTAMP:20260419T181423
CREATED:20230110T182620Z
LAST-MODIFIED:20230110T182620Z
UID:1506-1673613000-1673618400@isg.ics.uci.edu
SUMMARY:Andrew Chio: SmartSPEC: Customizable Smart Space Datasets via Event-Driven Simulations
DESCRIPTION:Bio – Andrew is a 4th year Ph.D. student in the Distributed Systems Middleware (DSM) group under the supervision of Professor Nalini Venkatasubramanian. His general research interests revolve around middleware\, data mining and analytics\, optimization\, and machine learning. \nAbstract – In this talk\, we present SmartSPEC\, an approach to generate customizable smart space datasets using sensorized spaces in which people and events are embedded. Smart space datasets are critical to design\, deploy and evaluate robust systems and applications to ensure cost-effective operation and safety/comfort/convenience of the space occupants. Often\, real-world data is difficult to obtain due to the lack of fine-grained sensing; privacy/security concerns prevent the release and sharing of individual and spatial data. SmartSPEC is a smart space simulator and data generator that can create a digital representation(twin) of a smart space and its activities. SmartSPEC uses a semantic model and ML-based approaches to characterize and learn attributes in a sensorized space\, and applies an event-driven simulation strategy to generate realistic simulated data about the space (events\, trajectories\, sensor datasets\, etc). To evaluate the realism of the data generated by SmartSPEC\, we develop a structured methodology and metrics to assess various aspects of smart space datasets\, including trajectories of people and occupancy of spaces. Our experimental study looks at two real-world settings/datasets: an instrumented smart campus building and a city-wide GPS dataset. Our results show that the trajectories produced by SmartSPEC are 1.4x to 4.4x more realistic than the best synthetic data baseline when compared to real-world data\, depending on the scenario and configuration.
URL:https://isg.ics.uci.edu/event/andrew-chio-smartspec-customizable-smart-space-datasets-via-event-driven-simulations/
LOCATION:DBH 4011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230120T130000
DTEND;TZID=America/Los_Angeles:20230120T140000
DTSTAMP:20260419T181423
CREATED:20230117T181251Z
LAST-MODIFIED:20230117T181251Z
UID:1509-1674219600-1674223200@isg.ics.uci.edu
SUMMARY:Tung-Chun Chang: SmartParcels: Cross-Layer IoT Planning for Smart Communities
DESCRIPTION:Abstract:\nThe emergence of IoT-aided smart communities has created the need for a new set of urban planning tools. The extra design process includes instrumenting infrastructures (sensing\, networking\, and computing devices) in smartspaces to generate information units (from data analytics) to realize a range of required services. We propose SmartParcels\, a framework that generates a comprehensive and cost-effective plan for instrumenting designated regions of smart communities (often called parcels). SmartParcels embeds an approach to solve the cross-layer IoT planning problem (shown to be NP-hard) that must consider applications\, information/data\, infrastructure\, and geophysical layout as interdependent layers in the overall design. We develop a suite of algorithms (optimal\, partial optimal\, heuristic) for the problem; urban planners can compose these techniques in a plug-and-play manner to achieve performance trade-offs (optimality\, timeliness). SmartParcels can be utilized for clean-slate planning (from scratch) or for retrofit of communities with existing smart infrastructure. We evaluate Smart- Parcels in two real-world settings: National Tsing Hua University in Taiwan and Irvine in California\, USA\, for clean-slate and retrofit. The evaluation results reveal that SmartParcels can enable a 2X – 7X improvement in cost/performance metrics as compared to the baseline algorithm in the clean-slate and retrofit cases.\n\nBio:\nTung-Chun Chang is a 4th year Ph.D. student in the Distributed Systems Middleware (DSM) group under the supervision of Professor Nalini Venkatasubramanian. His general research interests include the Internet of Things\, wireless networking\, network analysis\, social networks\, optimization\, and machine learning.
URL:https://isg.ics.uci.edu/event/tung-chun-chang-smartparcels-cross-layer-iot-planning-for-smart-communities/
LOCATION:DBH 4011
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230126T153000
DTEND;TZID=America/Los_Angeles:20230126T170000
DTSTAMP:20260419T181423
CREATED:20230123T193002Z
LAST-MODIFIED:20230123T193002Z
UID:1513-1674747000-1674752400@isg.ics.uci.edu
SUMMARY:Aaron Elmore: Adventures in Database Compression
DESCRIPTION:Prof. Aaron Elmore\n\nUniversity of Chicago\nAbstract: Columnar databases enable effective compression by improving entropy through attribute locality and provides opportunities for fast query execution directly on compressed data. In this talk I will briefly overview how compressed query execution works in columnar systems and discuss techniques developed by our group over the past several years. This includes a pattern-inferred attribute decomposition for improved string compression and query performance\, a bounded float compression technique for fast filtering on limited precision numeric data\, and partially ordered dictionary compression.
URL:https://isg.ics.uci.edu/event/aaron-elmore-adventures-in-database-compression/
LOCATION:TBD
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20230127T110000
DTEND;TZID=America/Los_Angeles:20230127T120000
DTSTAMP:20260419T181423
CREATED:20230123T192752Z
LAST-MODIFIED:20230123T192752Z
UID:1511-1674817200-1674820800@isg.ics.uci.edu
SUMMARY:Aaron Elmore: CrocodileDB: Resource Efficient Database Execution (CS Seminar)
DESCRIPTION:Prof. Aaron Elmore\nUniversity of Chicago\n\nAbstract: The coming end of Moore’s law requires that data systems be more judicious with computation and resources as the growth in data outpaces the availability of computational resources. Current database systems are eager and aggressively consume resources to immediately and quickly complete the task at hand. Intelligently deferring a task to a later point in time can increase result reuse\, reduce work that might later be invalidated\, or avoid unnecessary work altogether. In this talk I will introduce CrocodileDB\, a resource-efficient database system that automatically optimizes deferment based on user-specification and workload prediction. CrocodileDB integrates new ways of specifying timing information\, new query execution policies\, new task schedulers\, and new data loading schemes. In particular\, this talk will highlight two new query execution paradigms\, Intermittent Query Processing and Incremental-Aware Query Execution.
URL:https://isg.ics.uci.edu/event/aaron-elmore-crocodiledb-resource-efficient-database-execution-cs-seminar/
LOCATION:DBH 6011
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