Record linkage, also known as database matching or entity resolution, is now recognised as a core step in the KDD process. Data mining projects increasingly require that information from several sources is combined before the actual mining can be conducted. Also of increasing interest is the deduplication of a single database. The objectives of record linkage and deduplication are to identify, match and merge all records that relate to the same real-world entities. Because real-world data is commonly ‘dirty’, data cleaning is an important first step in many deduplication, record linkage, and data mining projects. In this paper, an overview of the Febrl (Freely Extensible Biomedical Record Linkage) system is provided, and the results of a recent survey of Febrl users is discussed. Febrl includes a variety of functionalities required for data cleaning, deduplication and record linkage, and it provides a graphical user interface that facilitates its application for users who do not have programming experience.

Abstract. Geocoding is the process of matching addresses to geographic locations, such as latitudes and longitudes, or local census areas. In many applications, addresses are the key to geo-spatial data analysis and mining. Privacy and confidentiality are of paramount importance when data from, for example, cancer registries or crime databases is geocoded. Various approaches to privacy-preserving data matching, also called record linkage or entity resolution, have been developed in recent times. However, most of these approaches have not considered the specific privacy issues involved in geocode matching. This paper provides a brief introduction to privacy-preserving data and geocode matching, and using several real-world scenarios the issues involved in privacy and confidentiality for data and geocode matching are illustrated. The challenges of making privacy-preserving matching practical for real-world applications are highlighted, and potential directions for future research are discussed.

Wikis allow for collaborators to collect information about entities. In turn, such entity information can be used for AI tasks, such as information extraction. However, these collaborators are almost exclusively human users. Allowing arbitrary software agents to act as collaborators can greatly enrich a wiki since agents can contribute structured data to complement the human-contributed, unstructured-data. For instance, agents can import huge volumes of structured data about entities, enriching the pages, and agents can update wiki pages to reflect real-time information changes (e.g., win-loss records in sports). This paper describes an approach that allows for both arbitrary software agents and human users to collaborate. In particular, we address three key problems: agents updating the correct wiki pages, policies for agent updates, and sharing the schema across collaborators. Using our approach, we describe creating entity-focused wikis which include the ability to create dynamic categories of entities based on their wiki pages. These categories dynamically update their membership based upon real-world changes.

In current world, the immense size of a data set makes problems in finding similar/identitcal data. In addition, the dirtiness of data, i.e. typos, missing/tilting information, and additional noises usually occurred by careless editing or entry mistakes, makes further difficulty to identify entity-belongs. Therefore, we focus on the faster detection of data referring the same real-world entity from a large size data set under the error prone environments, while the high accuracy of detection is maintained. In this thesis, we study high-performance linkage algorithms using four different applications. First, we introduce the image linkage algorithm to find near-duplicate images with similar characteristics by bridging two seemingly unrelated fields – Multimedia Information Retrieval and Biology. Under this idea, we study how various image features and gene sequence generation methods affect the accuracy and performance of detecting near-duplicate images. Second, we develop the video linkage algorithm using record linkage methods to detect copied videos from a large multi-media database or sites such as YouTube and Yahoo Videos. The utilization of video characteristics is reflected to the hierarchical structure of

Abstract—Organizations rely on data analysts to model customer engagement, streamline operations, improve production, inform business decisions, and combat fraud. Though numerous analysis and visualization tools have been built to improve the scale and efficiency at which analysts can work, there has been little research on how analysis takes place within the social and organizational context of companies. To better understand the enterprise analysts ’ ecosystem, we conducted semi-structured interviews with 35 data analysts from 25 organizations across a variety of sectors, including healthcare, retail, marketing and finance. Based on our interview data, we characterize the process of industrial data analysis and document how organizational features of an enterprise impact it. We describe recurring pain points, outstanding challenges, and barriers to adoption for visual analytic tools. Finally, we discuss design implications and opportunities for visual analysis research. Index Terms—Data, analysis, visualization, enterprise. 1