Scalable Audience Reach Estimation in Real-time Online Advertising

Online advertising has been introduced as one of the most efficient methods of advertising throughout the recent years. Yet, advertisers are concerned about the efficiency of their online advertising campaigns and consequently, would like to restrict their ad impressions to certain websites and/or certain groups of audience. These restrictions, known as targeting criteria, limit the reachability for better performance. This trade-off between reachability and performance illustrates a need for a forecasting system that can quickly predict/estimate (with good accuracy) this trade-off. Designing such a system is challenging due to (a) the huge amount of data to process, and, (b) the need for fast and accurate estimates. In this paper, we propose a distributed fault tolerant system that can generate such estimates fast with good accuracy. The main idea is to keep a small representative sample in memory across multiple machines and formulate the forecasting problem as queries against the sample. The key challenge is to find the best strata across the past data, perform multivariate stratified sampling while ensuring fuzzy fall-back to cover the small minorities. Our results show a significant improvement over the uniform and simple stratified sampling strategies which are currently widely used in the industry

Analytic Extensions to the Data Model for Management Analytics and Decision Support in the Big Data Environment

From 2006 to 2016, an estimated average of 50% of big data analytics and decision support projects failed to deliver acceptable and actionable outputs to business users. The resulting management inefficiency came with high cost, and wasted investments estimated at $2.7 trillion in 2016 for companies in the United States. The purpose of this quantitative descriptive study was to examine the data model of a typical data analytics project in a big data environment for opportunities to improve the information created for management problem-solving. The research questions focused on finding artifacts within enterprise data to model key business scenarios for management action. The foundations of the study were information and decision sciences theories, especially information entropy and high-dimensional utility theories. The design-based research in a nonexperimental format was used to examine the data model for the functional forms that mapped the available data to the conceptual formulation of the management problem by combining ontology learning, data engineering, and analytic formulation methodologies. Semantic, symbolic, and dimensional extensions emerged as key functional forms of analytic extension of the data model. The data-modeling approach was applied to 15-terabyte secondary data set from a multinational medical product distribution company with profit growth problem. The extended data model simplified the composition of acceptable analytic insights, the derivation of business solutions, and the design of programs to address the ill-defined management problem. The implication for positive social change was the potential for overall improvement in management efficiency and increasing participation in advocacy and sponsorship of social initiatives

Approximate query processing using machine learning

In the era of big data, the volume of collected data grows faster than the growth of computational power. And it becomes prohibitively expensive to compute the exact answers to analytical queries. This greatly increases the value of approaches that can compute efficiently approximate, but highly accurate, answers to analytical queries. Approximate query processing (AQP) aims to reduce the query latency and memory footprints at the cost of small quality losses. Previous efforts on AQP largely rely on samples or sketches, etc. However, trade-offs between query response time (or memory footprint) and accuracy are unavoidable. Specifically, to guarantee higher accuracy, a large sample is usually generated and maintained, which leads to increased query response time and space overheads. In this thesis, we aim to overcome the drawbacks of current AQP solutions by applying machine learning models. Instead of accessing data (or samples of it), models are used to make predictions. Our model-based AQP solutions are developed and improved in three stages, and are described as follows: 1. We firstly investigate potential regression models for AQP and propose the query-centric regression, coined QReg. QReg is an ensemble method based on regression models. It achieves better accuracy than the state-of- the-art regression models and overcomes the generalization-overfit dilemma when employing machine learning models within DBMSs. 2. We introduce the first AQP engine DBEst based on classical machine learning models. Specifically, regression models and density estimators are trained over the data/samples, and are further combined to produce the final approximate answers. 3. We further improve DBEst by replacing classical machine learning models with deep learning networks and word embedding. This overcomes the drawbacks of queries with large groups, and query response time and space overheads are further reduced. We conduct experiments against the state-of-the-art AQP engines over various datasets, and show that our method achieves better accuracy while offering orders of magnitude savings in space overheads and query response time

Fast Data Analytics by Learning

Today, we collect a large amount of data, and the volume of the data we collect is projected to grow faster than the growth of the computational power. This rapid growth of data inevitably increases query latencies, and horizontal scaling alone is not sufficient for real-time data analytics of big data. Approximate query processing (AQP) speeds up data analytics at the cost of small quality losses in query answers. AQP produces query answers based on synopses of the original data. The sizes of the synopses are smaller than the original data; thus, AQP requires less computational efforts for producing query answers, thus can produce answers more quickly. In AQP, there is a general tradeoff between query latencies and the quality of query answers; obtaining higher-quality answers requires longer query latencies. In this dissertation, we show we can speed up the approximate query processing without reducing the quality of the query answers by optimizing the synopses using two approaches. The two approaches we employ for optimizing the synopses are as follows: 1. Exploiting past computations: We exploit the answers to the past queries. This approach relies on the fact that, if two aggregation involve common or correlated values, the aggregated results must also be correlated. We formally capture this idea using a probabilistic distribution function, which is then used to refine the answers to new queries. 2. Building task-aware synopses: By optimizing synopses for a few common types of data analytics, we can produce higher quality answers (or more quickly for certain target quality) to those data analytics tasks. We use this approach for constructing synopses optimized for searching and visualizations. For exploiting past computations and building task-aware synopses, our work incorporates statistical inference and optimization techniques. The contributions in this dissertation resulted in up to 20x speedups for real-world data analytics workloads.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138598/1/pyongjoo_1.pd

Query-driven learning for automating exploratory analytics in large-scale data management systems

As organizations collect petabytes of data, analysts spend most of their time trying to extract insights. Although data analytic systems have become extremely efficient and sophisticated, the data exploration phase is still a laborious task with high productivity, monetary and mental costs. This dissertation presents the Query-Driven learning methodology in which multiple systems/frameworks are introduced to address the need of more efficient methods to analyze large data sets. Countless queries are executed daily, in large deployments, and are often left unexploited but we believe they are of immense value. This work describes how Machine Learning can be used to expedite the data exploration process by (a) estimating the results of aggregate queries (b) explaining data spaces through interpretable Machine Learning models (c) identifying data space regions that could be of interest to the data analyst. Compared to related work in all the associated domains, the proposed solutions do not utilize any of the underlying data. Because of that, they are extremely efficient, decoupled from underlying infrastructure and can easily be adapted. This dissertation is a first account of how the Query-Driven methodology can be effectively used to expedite the data exploration process focusing solely on extracting knowledge from queries and not from data

Multi-dimensional mining of unstructured data with limited supervision

As one of the most important data forms, unstructured text data plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to healthcare and scientific research. In many emerging applications, people's information needs from text data are becoming multi-dimensional---they demand useful insights for multiple aspects from the given text corpus. However, turning massive text data into multi-dimensional knowledge remains a challenge that cannot be readily addressed by existing data mining techniques. In this thesis, we propose algorithms that turn unstructured text data into multi-dimensional knowledge with limited supervision. We investigate two core questions: 1. How to identify task-relevant data with declarative queries in multiple dimensions? 2. How to distill knowledge from data in a multi-dimensional space? To address the above questions, we propose an integrated cube construction and exploitation framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multi-dimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling multi-dimensional knowledge from data to provide insights along multiple dimensions. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multi-dimensional, multi-granular data selection with declarative queries; and with cube exploitation algorithms, users can make accurate cross-dimension predictions or extract multi-dimensional patterns for decision making. The proposed framework has two distinctive advantages when turning text data into multi-dimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multi-dimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multi-dimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain

Multikonferenz Wirtschaftsinformatik (MKWI) 2016: Technische Universität Ilmenau, 09. - 11. März 2016; Band II

Übersicht der Teilkonferenzen Band II • eHealth as a Service – Innovationen für Prävention, Versorgung und Forschung • Einsatz von Unternehmenssoftware in der Lehre • Energieinformatik, Erneuerbare Energien und Neue Mobilität • Hedonische Informationssysteme • IKT-gestütztes betriebliches Umwelt- und Nachhaltigkeitsmanagement • Informationssysteme in der Finanzwirtschaft • IT- und Software-Produktmanagement in Internet-of-Things-basierten Infrastrukturen • IT-Beratung im Kontext digitaler Transformation • IT-Sicherheit für Kritische Infrastrukturen • Modellierung betrieblicher Informationssysteme – Konzeptuelle Modelle im Zeitalter der digitalisierten Wirtschaft (d!conomy) • Prescriptive Analytics in I