Physical Representation-based Predicate Optimization for a Visual Analytics Database

Querying the content of images, video, and other non-textual data sources requires expensive content extraction methods. Modern extraction techniques are based on deep convolutional neural networks (CNNs) and can classify objects within images with astounding accuracy. Unfortunately, these methods are slow: processing a single image can take about 10 milliseconds on modern GPU-based hardware. As massive video libraries become ubiquitous, running a content-based query over millions of video frames is prohibitive. One promising approach to reduce the runtime cost of queries of visual content is to use a hierarchical model, such as a cascade, where simple cases are handled by an inexpensive classifier. Prior work has sought to design cascades that optimize the computational cost of inference by, for example, using smaller CNNs. However, we observe that there are critical factors besides the inference time that dramatically impact the overall query time. Notably, by treating the physical representation of the input image as part of our query optimization---that is, by including image transforms, such as resolution scaling or color-depth reduction, within the cascade---we can optimize data handling costs and enable drastically more efficient classifier cascades. In this paper, we propose Tahoma, which generates and evaluates many potential classifier cascades that jointly optimize the CNN architecture and input data representation. Our experiments on a subset of ImageNet show that Tahoma's input transformations speed up cascades by up to 35 times. We also find up to a 98x speedup over the ResNet50 classifier with no loss in accuracy, and a 280x speedup if some accuracy is sacrificed.Comment: Camera-ready version of the paper submitted to ICDE 2019, In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE 2019

Combining Disparate Information for Machine Learning.

This thesis considers information fusion for four different types of machine learning problems: anomaly detection, information retrieval, collaborative filtering and structure learning for time series, and focuses on a common theme -- the benefit to combining disparate information resulting in improved algorithm performance. In this dissertation, several new algorithms and applications to real-world datasets are presented. In Chapter II, a novel approach called Pareto Depth Analysis (PDA) is proposed for combining different dissimilarity metrics for anomaly detection. PDA is applied to video-based anomaly detection of pedestrian trajectories. Following a similar idea, in Chapter III we propose to use a similar Pareto Front method for a multiple-query information retrieval problem when different queries represent different semantic concepts. Pareto Front information retrieval is applied to multiple query image retrieval. In Chapter IV, we extend a recently proposed collaborative retrieval approach to incorporate complementary social network information, an approach we call Social Collaborative Retrieval (SCR). SCR is applied to a music recommendation system that combines both user history and friendship network information to improve recall and weighted recall performance. In Chapter V, we propose a framework that combines time series data at different time scales and offsets for more accurate estimation of multiple precision matrices. We propose a general fused graphical lasso approach to jointly estimate these precision matrices. The framework is applied to modeling financial time series data.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108878/1/coolmark_1.pd

Intelligent search in social communities of smartphone users

Social communities of smartphone users have recently gained significant interest due to their wide social penetration. The applications in this domain,however, currently rely on centralized or cloud-like architectures for data sharing and searching tasks, introducing both data-disclosure and performance concerns. In this paper, we present a distributed search architecture for intelligent search of objects in a mobile social community. Our framework, coined SmartOpt, is founded on an in-situ data storage model, where captured objects remain local on smartphones and searches then take place over an intelligent multi-objective lookup structure we compute dynamically. Our MO-QRT structure optimizes several conflicting objectives, using a multi-objective evolutionary algorithm that calculates a diverse set of high quality non-dominated solutions in a single run. Then a decision-making subsystem is utilized to tune the retrieval preferences of the query user. We assess our ideas both using trace-driven experiments with mobility and social patterns derived by Microsoft’s GeoLife project, DBLP and Pics ‘n’ Trails but also using our real Android SmartP2P3 system deployed over our SmartLab4 testbed of 40+ smartphones. Our study reveals that SmartOpt yields high query recall rates of 95%, with one order of magnitude less time and two orders of magnitude less energy than its competitors

A Survey of Techniques for Answering Top-K Queries

Top-k queries are useful in retrieving top-k records from a given set of records depending on the value of a function F on their attributes. Many techniques have been proposed in database literature for answering top-k queries. These are mainly categorized into three: Sorted-list based, layer based and View based. In first category, records are sorted along each dimension and then assigned a rank to each of the records using parallel scanning method. Threshold Algorithm (TA) and Fagin2019;s Algorithm (FA) are the examples of sorted-list based category. Second category is layer based category, in which all the records are organized into layers such as in onion technique and robust indexing technique. Third category includes methods such as PREFER and LPTA (Linear Programming Adaptation of Threshold Algorithm) and processing is based on the materialized views