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Elf is a runtime for an energy-constrained camera to continuously summarize video scenes as approximate object counts. Elfs novelty centers on planning the cameras count actions under energy constraint. (1) Elf explores the rich action space spanned by the number of sample image frames and the choice of per-frame object counters; it unifies errors from both sources into one single bounded error. (2) To decide count actions at run time, Elf employs a learning-based planner, jointly optimizing for past and future videos without delaying result materialization. Tested with more than 1,000 hours of videos and under realistic energy constraints, Elf continuously generates object counts within only 11% of the true counts on average. Alongside the counts, Elf presents narrow errors shown to be bounded and up to 3.4x smaller than competitive baselines. At a higher level, Elf makes a case for advancing the geographic frontier of video analytics.
We study the hardness of Approximate Query Processing (AQP) of various types of queries involving joins over multiple tables of possibly different sizes. In the case where the query result is a single value (e.g., COUNT, SUM, and COUNT(DISTINCT)), we
Sample-based approximate query processing (AQP) suffers from many pitfalls such as the inability to answer very selective queries and unreliable confidence intervals when sample sizes are small. Recent research presented an intriguing solution of com
We have developed two redundant daytime star cameras to provide the fine pointing solution for the balloon-borne submillimeter telescope, BLAST. The cameras are capable of providing a reconstructed pointing solution with an absolute accuracy < 5 arcs
We study persistent query evaluation over streaming graphs, which is becoming increasingly important. We focus on navigational queries that determine if there exists a path between two entities that satisfies a user-specified constraint. We adopt the
We investigate the computational complexity of minimizing the source side-effect in order to remove a given number of tuples from the output of a conjunctive query. In particular, given a multi-relational database $D$, a conjunctive query $Q$, and a