The Applied Data Science (ADS) Invited Talks Track at KDD-2017 is a continuation of what has now become a "7-year tradition" at KDD conferences. This is the second year the track operates under the ADS name, an evolution from its origins at KDD-2011 as the "Industry Practice Expo". The KDD Conference on Knowledge Discovery and Data Mining (KDD) is the world's first, largest and best conference on Data Science, Data Mining, and Knowledge Discovery. It brings together a healthy mix of academic researchers, industry and government researchers, and practitioners from a wide range of institutions and fields. The primary focus on KDD is on peer-reviewed research contributions and the academic advancement of the field. This is an important goal and in fact the KDD conference is now recognized as the most competitive and prestigious forum for presenting high quality research results. KDD, being fundamentally an applied field, needs the strong representation of applied work of big impact. Over the years of running the conference we observed that our initial speaker-selection approach needed to be re-thought because of the important contributions made to the field outside traditional academic, industrial and government research laboratories. The result of this re-thinking was to create a forum that exposes important contributions to Data Science through Big Data Applications that address strategic problems. We wanted to effectively capture the rising importance of Data Science and Machine Learning especially in the Big Data environment where structured and unstructured data create special challenges, and of course present new opportunities. The goal of the Invited Talks Track is to curate contributions from leaders in our field who have made important contributions through the development of a system, the creation of a new and important business, or the development and market introduction of a product,. Some of these important contributions may never see an academic paper or detailed peer-reviewed paper written about them, yet they are of critical importance to our very applied field. To give you an idea of how rapidly growing this area is, and how this sector of our industry and promises to be highly disruptive across many industries, we cite a couple of articles out of a plethora of such coverage: According to IDC, the global revenues from Big Data and business will grow from $130.1 billion in 2016 to more than $203 billion in 2020, at a compound annual growth rate (CAGR) of 11.7% [1]. Furthermore, to quote from a Forbes article: "Data monetization" will become a major source of revenues, as the world will create 180 zettabytes of data (or 180 trillion gigabytes) in 2025, up from less than 10 zettabytes in 2015.? [2]

No abstract available.

Summary form only given, as follows. Data mining applications deployed in industry are aimed at satisfying two problems organizations face: customer intimacy and better utilization of data assets. These applications can be divided into those that use micro-mining, i.e. single-mining-component desktop systems, and those who use macro-mining, i.e. multi-component server-based systems. The macro-mining applications are usually coupled with data warehouses. The interesting result of this coupling for the data mining community is that the data warehouses cannot be supported by the current data mining offerings delaying the deployment of applications in production environments. The data volumes are too large, the data types too diverse and the data characteristics too incompatible for the existing data mining algorithms. Furthermore, the pure mining operation is a very small part of the entire application life-cycle. The author presents the issues related to the coupling of macro-mining with data warehouses, and proposes issues that must be resolved for large-scale data mining applications to continue being deployed successfully.

This paper first provides an overview of data preprocessing, focusing on problems of real world data. These are primarily problems that have to be carefully understood and solved before any data analysis process can start. The paper discusses in detail two main reasons for performing data preprocessing: (i) problems with the data and (ii) preparation for data analysis. The paper continues with details of data preprocessing techniques achieving each of the above mentioned objectives. A total of 14 techniques are discussed. Two examples of data preprocessing applications from two of the most data rich domains are given at the end. The applications are related to semiconductor manufacturing and aerospace domains where large amounts of data are available, and they are fairly reliable. Future directions and some challenges are discussed at the end.

Over the past ten years a myriad of knowledge-based expert systems have been developed and deployed. These systems have a narrow scope and usually operate in stand-alone mode. They also follow different implementation philosophies and use a variety of reasoning methods. To address problems of wider scope, researchers have developed systems that utilize either centralized or distributed computational models. Each of these systems is homogeneous, and due to the way developed, prohibitively expensive for real-world settings. In this paper we present OMNI, a framework for integrating existing knowledge-based systems in a way that they can cooperate during problem-solving while they remain distributed over a computing environment.

A learning component that has been incorporated into the transmission line troubleshooting system (TLTS), a knowledge-based system which implements a model for performing redesign, is presented. The model was applied in the domain of redesigning circuits which are laid on printed circuit boards. The knowledge of TLTS is organized in a blackboard architecture. The learning component can form new knowledge sources from redesign knowledge. It does this by compiling the knowledge that is contained in redesign plans that the user creates in cooperation with TLTS. The learning component also acquires redesign metaknowledge for selecting the most appropriate knowledge source to redesign a faulty circuit. It does this by utilizing the problem-solving information that is generated by TLTS and cooperating with the user.>

A methodology is presented for evaluating and redesigning digital circuit networks with signal integrity problems and a tool which implements this methodology. Evaluation consists of simulating the behavior of the circuit, analyzing the simulation results to reveal signal integrity problems, and diagnosing the causes of each problem. Redesign consists of finding an appropriate method to modify the structure of a faulty circuit based on the evaluation results, forming a plan out of the operators which are included in the method and applying the plan. Evaluation and redesign of the circuit are performed in two abstraction levels. Part of the work has gone into identifying and implementing the types of circuit knowledge that are necessary for the realization of the methodology. The tool which implements the methodology is called the transmission-line troubleshooting system (TLTS). TLTS executes the evaluation-redesign loop until a problem-free circuit is obtained. TLTS is implemented in the expert-system language ORBS.>