Entity matching (EM) finds data instances that refer to the same real-world entity. In 2015, we started the Magellan project at UW-Madison, joint with industrial partners, to build EM systems. Most current EM systems are stand-alone monoliths. In contrast, Magellan borrows ideas from the field of data science (DS), to build a new kind of EM systems, which is an ecosystem of interoperable tools. \em This paper provides a progress report on the past 3.5 years of Magellan, focusing on the system aspects and on how ideas from the field of data science have been adapted to the EM context. We argue why EM can be viewed as a special class of DS problems, and thus can benefit from system building ideas in DS. We discuss how these ideas have been adapted to build \pymatcher\ and \cloudmatcher, EM tools for power users and lay users. These tools have been successfully used in 21 EM tasks at 12 companies and domain science groups, and have been pushed into production for many customers. We report on the lessons learned, and outline a new envisioned Magellan ecosystem, which consists of not just on-premise Python tools, but also interoperable microservices deployed, executed, and scaled out on the cloud, using tools such as Dockers and Kubernetes.

Entity matching (EM) finds data instances that refer to the same real-world entity. In this paper we examine applying deep learning (DL) to EM, to understand DL's benefits and limitations. We review many DL solutions that have been developed for related matching tasks in text processing (e.g., entity linking, textual entailment, etc.). We categorize these solutions and define a space of DL solutions for EM, as embodied by four solutions with varying representational power: SIF, RNN, Attention, and Hybrid. Next, we investigate the types of EM problems for which DL can be helpful. We consider three such problem types, which match structured data instances, textual instances, and dirty instances, respectively. We empirically compare the above four DL solutions with Magellan, a state-of-the-art learning-based EM solution. The results show that DL does not outperform current solutions on structured EM, but it can significantly outperform them on textual and dirty EM. For practitioners, this suggests that they should seriously consider using DL for textual and dirty EM problems. Finally, we analyze DL's performance and discuss future research directions.

As data science applications proliferate, more and more lay users must perform data integration (DI) tasks, which used to be done by sophisticated CS developers. Thus, it is increasingly critical that we develop hands-off DI services, which lay users can use to perform such tasks without asking for help from developers. We propose to demonstrate such a service. Specifically, we will demonstrate CloudMatcher, a hands-off cloud/crowd service for entity matching (EM). To use CloudMatcher to match two tables, a lay user only needs to upload them to the CloudMatcher's Web page then iteratively label a set of tuple pairs as match/no-match. Alternatively, the user can enlist a crowd of workers to label the pairs. In either case, the lay user can easily perform EM end-to-end without having to involve any developers. Cloud-Matcher has been used in several domain science projects at UW-Madison and at several organizations, and is scheduled to be deployed in a large company in Summer 2018. In the demonstration we will show how easy it is for lay users to perform EM (either via interactive labeling or crowdsourcing), how users can easily create and experiment with a range of EM workflows, and how CloudMatcher can scale to many concurrent users and large datasets.

Many works have applied crowdsourcing to entity matching (EM). While promising, these approaches are limited in that they often require a developer to be in the loop. As such, it is difficult for an organization to deploy multiple crowdsourced EM solutions, because there are simply not enough developers. To address this problem, a recent work has proposed Corleone, a solution that crowdsources the entire EM workflow, requiring no developers. While promising, Corleone is severely limited in that it does not scale to large tables. We propose Falcon, a solution that scales up the hands-off crowdsourced EM approach of Corleone, using RDBMS-style query execution and optimization over a Hadoop cluster. Specifically, we define a set of operators and develop efficient implementations. We translate a hands-off crowdsourced EM workflow into a plan consisting of these operators, optimize, then execute the plan. These plans involve both machine and crowd activities, giving rise to novel optimization techniques such as using crowd time to mask machine time. Extensive experiments show that Falcon can scale up to tables of millions of tuples, thus providing a practical solution for hands-off crowdsourced EM, to build cloud-based EM services.

Summary form only given. Devices, people, information and software applications rarely live in isolation in modern building management. For example, networked sensors that monitor the performance of a chiller are common and collected data are delivered to building automation systems to optimize energy use. Detected possible failures are also handed to facility management staffs for repairs. Physical and cyber security services have to be incorporated to prevent improper access of not only HVAC (Heating, Ventilation, Air Conditioning) equipment but also control devices. Harmonizing these connected sensors, control devices, equipment and people is a key to provide more comfortable, safe and sustainable buildings. Nowadays, devices with embedded intelligences and communication capabilities can interact with people directly. Traditionally, few selected people (e.g., facility managers in building industry) have access and program the device with fixed operating schedule while a device has a very limited connectivity to an operating environment and context. Modern connected devices will learn and interact with users and other connected things. This would be a fundamental shift in ways in communication from unidirectional to bi-directional. A manufacturer will learn how their products and features are being accessed and utilized. An end user or a device on behalf of a user can interact and communicate with a service provider or a manufacturer without go though a distributer, almost real time basis. This will requires different business strategies and product development behaviors to serve connected customers' demands. Connected things produce enormous amount of data that result many questions and technical challenges in data management, analysis and associated services. In this talk, we will brief some of challenges that we have encountered In developing connected building solutions and services. More specifically, (1) semantic interoperability requirements among smart sensors, actuators, lighting, security and control and business applications, (2) engineering challenges in managing massively large time sensitive multi-media data in a cloud at global scale, and (3) security and privacy concerns are presented.

Cameras are becoming a common tool for automated vision purposes due to their low cost. In an era of growing security concerns, camera surveillance systems have become not only important but also necessary. Algorithms for several tasks such as detecting abandoned objects and tracking people have already been successfully developed. While tracking people is relatively easy, counting people in groups is much more challenging. The mutual occlusions between people in a group make it difficult to provide an exact count. The aim of this work is to present a method of estimating the number of people in group scenarios. Several considerations for counting people are illustrated in this paper, and experimental results of the method are described and discussed.

In this paper, we present an efficient term rewriting technique that computes a degree of term to domain relevance. The proposed method resolves the problems in ontology integrated concept search. Those problems are (i) Pre-defined concept classes in ontology are not relevant to users (no proper concept class for a target annotation has not found). (ii) Too many similar concept classes are provided to a user therefore, a user may fail to choose a correct semantic class for a target annotation (ordinary users are not an expert in concept classification). The method uses sense disambiguation task for finding relevant terms for a given domain. Sense disambiguation requires term-to-term similarity measurement and term frequency measurement. For fair modeling of not observed term frequencies, discounting and redistribution model is applied. The proposed method is a compliment to our previous work presented in [13][14]. Robustness of our method is demonstrated through human judgment test that shows our method allows prediction of precise term list (overall 75% of correct prediction) that are relevant to a given domain.

Noting the shortage of IT professionals nationally [1], we propose a comprehensive curriculum that supports a variety of programs geared to all ages from early school years to retirement and beyond. Current IT workforce development efforts are limited to training, and have not as yet focused on education and professional development. Largely, this is due to a lack of a science underpinning for IT related curricula. Without such a unified science component, a structured organization of information related concepts cannot be derived.Our proposal includes the development of a number of programs addressing the needs of a variety of learners ranging from elementary school through college and beyond. Seven programs, each with a specific emphasis for various groups, are being developed. Such essential issues as industrial-academic liaisons, workforce (re)training, promotional and awareness programs, teacher training, and IT professional role redefinition, are integral pieces of this project. All developments will be firmly founded on the scientific framework of information science and engineering [2].This work is supported by NSF grant DUE-9950168.

Human color perception is subjective. In addition to RGB or HSV values representing color content, psychological factors, circumstantial factors, environmental factors, and physiological factors play an important role in encapsulating the color content. Typically, only the RGB or HSV values are used in indexing and retrieval of color images. In this paper, we demonstrate the superior retrieval performance of techniques which employ all of the above factors in color retrieval. We first present the variety of factors involved in human color perception and also an evaluation of the existing color indexing methods. Several interesting problems including comparison of images in the color-perceptual domain and retrieval by color affection are illustrated as potential novel image query mechanisms.