The 3rd IADSS Workshop on Data Science Standards follows a tradition of two prior KDD workshops and the initial workshop at ICDM-2018. The theme of the 2022 workshop is: Hiring, Assessing and Upskilling Data Science Talent. Organized by the Initiative for Analytics and Data Science (IADSS.org) at KDD, the workshop provides a platform to discuss industry needs and practices around external and internal talent pipeline development in data science. We aim to provide an understanding of the data science job market, and the critical role of collaboration between academic institutions and industry to meet the increasing need for talent. IADSS conducts ongoing research in this domain. In this workshop, we share detailed findings and observations from this research. In addition to contribution from researchers and industry practitioners through an open call for papers, the workshop features several invited presentations and invited speakers. In order to achieve intended aim, interactive panels will discuss topics of interest and feedback from the workshop will be used to produce post-workshop learnings. This workshop is designed as a half-day working meeting with short talks, invited panels and discussion sessions to plan for future steps in the topic. Post conference, learnings from the workshop will be available at the workshop's home page: https://www.iadss.org/kdd2022

This panel aims to address areas that are widely acknowledged to be of critical importance to the success of Data Science projects and to the healthy growth of KDD/Data Science as a field of scientific research. However, despite this acknowledgement of their criticality, these areas receive insufficient attention in the major conferences in the field. Furthermore, there is a lack of actual actions and tools to address these areas in actual practice. These areas are summarized as follows:

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]

Human is surrounded by a tremendous amount of information on the web. That highlights the continuous need of recommendation systems in the different domains. Unfortunately cold start problem is still an important issue in these systems on new users and new items. The problem becomes more critical in systems that contain resources that lives too shortly like offers on products which stays only for few days (short life resources - SLiR), or news in a news site. From the other side social networks are very rich with users' information, unfortunately most of the proposed social recommender are applied on domain specific social networks like flickers and epinions which are much less used in the day to day life, because dealing with General Purpose Social Network (GPSN) like Facebook and Twitter needs to transform these GPSN into a useful source of recommendation dealing with them as row, implicit or unary data. In this work we highlight how iSoNTRE (the intelligent Social Network Transformer into Recommendation Engine) addresses this challenge by transforming the GPSN into useful information for recommendation based on middle layer of domain concepts. iSoNTRE overcomes the cold start problem on new users and items. It has been evaluated over Twitter, on new users, recommending offers as a kind of SLiR, results showed that iSoNTRE succeeded in recommending good offers with 14% of click on recommended offers, which is high compared to general open rate in social media, especially when we have nothing about users and we are recommending SLiR resources.

Human is surrounded by a tremendous and scary amount of information on the web. That highlights the continuous need of recommendation systems in the different domains. Unfortunately cold start problem is still an important issue in these systems on new users and new items. The problem becomes more critical in systems that contain resources that lives too shortly like offers on products which stays only for few days (short life resources-SLiR). In this work we highlight how iSoNTRE (the intelligent Social Network Transformer into Recommendation Engine) solves this problem by using users' information in online social networks to overcome the cold start problem on new users, as well as iSoNTRE uses conceptual similarity, this overcomes the problem on new items, and on short life resources also. The work has been evaluated on Twitter on real users and results show that iSoNTRE succeeded in recommending offers to users with 14% of open rate on recommended offers, which is high compared to general open rate in social media, especially when we have nothing about users or offers before.

This is an abstract of the Invited Keynote Presentation to be presented at KDD-07.

The integration of data mining with traditional database systems is key to making it convenient, easy to deploy in real applications, and to growing its user base. We describe the new API for data mining proposed by Microsoft as extensions to the OLE DB standard. We illustrate the basic notions that motivated the API's design and describe the key components of an OLE DB for the data mining provider. We also include examples of the usage and treat the problems of data representation and integration with the SQL framework. We believe this new API will go a long way in enabling deployment of data mining in enterprise data warehouses. A reference implementation of a provider is available with the recent release of Microsoft SQL Server 2000 database system.

We present a generalization of frequent itemsets allowing for the notion of errors in the itemset definition. We motivate the problem and present an efficient algorithm that identifies error-tolerant frequent clusters of items in transactional data (customer-purchase data, web browsing data, text, etc.). The algorithm exploits sparseness of the underlying data to find large groups of items that are correlated over database records (rows). The notion of transaction coverage allows us to extend the algorithm and view it as a fast clustering algorithm for discovering segments of similar transactions in binary sparse data. We evaluate the new algorithm on three real-world applications: clustering high-dimensional data, query selectivity estimation and collaborative filtering. Results show that the algorithm consistently uncovers structure in large sparse databases that other traditional clustering algorithms fail to find.

Clustering very large databases is a challenge for traditional pattern recognition algorithms, e.g. the expectation-maximization (EM) algorithm for fitting mixture models, because of high memory and iteration requirements. Over large databases, the cost of the numerous scans required to converge and large memory requirement of the algorithm becomes prohibitive. We present a decomposition of the EM algorithm requiring a small amount of memory by limiting iterations to small data subsets. The scalable EM approach requires at most one database scan and is based on identifying regions of the data that are discardable, regions that are compressible, and regions that must be maintained in memory. Data resolution is preserved to the extent possible based upon the size of the memory buffer and fit of the current model to the data. Computational tests demonstrate that the scalable scheme outperforms similarly constrained EM approaches.

We identify data-intensive operations that are common to classifiers and develop a middleware that decomposes and schedules these operations efficiently using a backend SQL database. Our approach has the added advantage of not requiring any specialized physical data organization. We demonstrate the scalability characteristics of our enhanced client with experiments on Microsoft SQL Server 7.0 by varying data size, number of attributes and characteristics of decision trees.

We consider the problem of performing Nearest-neighbor queries efficiently over large high-dimensional databases.To avoid a full database scan, we target constructing a multidimensional index structure.It is well-accepted that traditional database indexing algorithms fail for high-dimensional data (say d > 10 or 20 depending on the scheme).Some arguments have advocated that nearest-neighbor queries do not even make sense for high-dimensional data.We show that these arguments are based on over-restrictive assumptions, and that in the general case it is meaningful and possible to build an index for such queries.Our approach, called DBIN, scales to high-dimensional databases by exploiting statistical properties of the data.The approach is based on statistically modeling the density of the content of the data table.DBIN uses the density model to derive a single index over the data table and requires physically rewriting data in a new table sorted by the newly created index (i.e.create a clustered-index).The indexing scheme produces a mapping between a query point (a data record) and an ordering on the clustered index values.Data is then scanned according to the index.We present theoretical and empirical justification for DBIN.The scheme supports a family of distance functions which includes the traditional Euclidean distance measure.

of pre-computation are to be realized.Efficiently answering decision support queries is an important problem.Most of the work in this direction has been in the context of the data cube.Queries are efficiently answered by pre-computing large parts of the cube.Besides having large space requirements, such pre-computation requires that the hierarchy along each dimension be fixed (hence dimensions are categorical or prediscretized).Queries that take advantage of pre-computation can thus only drill-down or roll-up along this fixed hierarchy.Another disadvantage of existing pre-computation techniques is that the target measure, along with the aggregation function of interest, is fixed for each cube.Queries over more than one target measure or using different aggregation functions, would require pre-computing larger data cubes.In this paper, we propose a new compressed representation of the data cube that (a) drastically reduces storage requirements, (b) does not require the discretization hierarchy along each query dimension to be fixed beforehand and (c) treats each dimension as a potential target measure and supports multiple aggregation functions without additional storage costs.The tradeoff is approximate, yet relatively accurate, answers to queries.We outline mechanjsms to reduce the error in the approximation.Our performance evaluation indicates that our compression technique effectively addresses the limitations of existing approaches.

This article is intended to serve as an overview of a rapidly emerging research and applications area. In addition to providing a general overview, motivating the importance of data mining problems within the area of knowledge discovery in databases, our aim is to list some of the pressing research challenges, and outline opportunities for contributions by the optimization research communities. Towards these goals, we include formulations of the basic categories of data mining methods as optimization problems. We also provide examples of successful mathematical programming approaches to some data mining problems.

Data mining and knowledge discovery in databases (KDD) promise to play an important role in the way people interact with databases, especially scientific databases where analysis and exploration operations are essential. The author defines the basic notions in data mining and KDD, defines the goals, presents motivation, and gives a high-level definition of the KDD process and how it relates to data mining. The author then focuses on data mining methods. Basic coverage of a sampling of methods is provided to illustrate the methods and how they are used. The author covers a case study of a successful application in science data analysis: the classification of cataloging of a major astronomy sky survey covering 2 billion objects in the northern sky. The system can outperform human as well as classical computational analysis tools in astronomy on the task of recognizing faint stars and galaxies. The author also covers the problem of scaling a clustering problem to a large catalog database of billions of objects.

Our long-term goal is to develop a trainable tool for locating patterns of interest in large image databases. Toward this goal we have developed a prototype system, based on classical filtering and statistical pattern recognition techniques, for automatically locating volcanoes in the Magellan SAR database of Venus. Training for the specific volcano-detection task is obtained by synthesizing feature templates (via normalization and principal components analysis) from a small number of examples provided by experts. Candidate regions identified by a focus of attention (FOA) algorithm are classified based on correlations with the feature templates. Preliminary tests show performance comparable to trained human observers.>

Lack of verifiable ground truth is a common problem in remote sensing image analysis. For example, consider the synthetic aperture radar (SAR) image data of Venus obtained by the Magellan spacecraft. Planetary scientists are interested in automatically cataloging the locations of all the small volcanoes in this data set; however, the problem is very difficult and cannot be performed with perfect reliability even by human experts. Thus, training and evaluating the performance of an automatic algorithm on this data set must be handled carefully. We discuss the use of weighted free-response receiver-operating characteristics (wFROCs) for evaluating detection performance when the "ground truth" is subjective. In particular, we evaluate the relative detection performance of humans and automatic algorithms. Our experimental results indicate that proper assessment of the uncertainty in "ground truth" is essential in applications of this nature.>

In remote sensing applications "ground-truth" data is often used as the basis for training pattern recognition algorithms to gener(cid:173) ate thematic maps or to detect objects of interest. In practical situations, experts may visually examine the images and provide a subjective noisy estimate of the truth. Calibrating the reliability and bias of expert labellers is a non-trivial problem. In this paper we discuss some of our recent work on this topic in the context of detecting small volcanoes in Magellan SAR images of Venus. Empirical results (using the Expectation-Maximization procedure) suggest that accounting for subjective noise can be quite signifi(cid:173) cant in terms of quantifying both human and algorithm detection performance.

Article Automated cataloging and analysis of sky survey image databases: the SKICAT system Share on Authors: Usama M. Fayyad AI Group M/S 525-3660, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA AI Group M/S 525-3660, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CAView Profile , Nicholas Weir Palomar Observatory, M/S 105-24, California Institute of Technology, Pasadena, CA Palomar Observatory, M/S 105-24, California Institute of Technology, Pasadena, CAView Profile , S. Djorgovski Palomar Observatory, M/S 105-24, California Institute of Technology, Pasadena, CA Palomar Observatory, M/S 105-24, California Institute of Technology, Pasadena, CAView Profile Authors Info & Claims CIKM '93: Proceedings of the second international conference on Information and knowledge managementDecember 1993 Pages 527–536https://doi.org/10.1145/170088.170414Online:01 December 1993Publication History 16citation504DownloadsMetricsTotal Citations16Total Downloads504Last 12 Months4Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access