This paper tackles the problem of recommendations in eBay's large semi-structured marketplace. eBay's variable inventory and lack of structured information about listings makes traditional collaborative filtering algorithms difficult to use. We discuss how to overcome these data limitations to produce high quality recommendations in real time with a combination of a customized scalable architecture as well as a widely applicable machine learned ranking model. A pointwise ranking approach is utilized to reduce the ranking problem to a binary classification problem optimized on past user purchase behavior. We present details of a sampling strategy and feature engineering that have been critical to achieve a lift in both purchase through rate (PTR) and revenue.

Ranked data is ubiquitous in real-world applications, arising naturally when users express preferences about products and services, when voters cast ballots in elections, and when funding proposals are evaluated based on their merits or university departments based on their reputation. This paper focuses on crowdsourcing and novel analysis of ranked data. We describe the design of a data collection task in which Amazon MT workers were asked to rank movies. We present results of data analysis, correlating our ranked dataset with IMDb, where movies are rated on a discrete scale rather than ranked. We develop an intuitive measure of worker quality appropriate for this task, where no gold standard answer exists. We propose a model of local structure in ranked datasets, reflecting that subsets of the workers agree in their ranking over subsets of the items, develop a data mining algorithm that identifies such structure, and evaluate in on our dataset. Our dataset is publicly available at https://github.com/stoyanovich/CrowdRank.

Preference data arises in a wide variety of domains. Over the past decade, we have seen a sharp increase in the volume of preference data, in the diversity of applications that use it, and in the richness of preference data analysis methods. Examples of applications include rank aggregation in genomic data analysis, management of votes in elections, and recommendation systems in e-commerce. However, little attention has been paid to the challenges of building a system for preference-data management, which would help incorporate sophisticated analytics into larger applications, support computational abstractions for usability by data scientists, and enable scaling up to modern volumes. This vision paper proposes a management system for preference data that aims to address these challenges. We adopt the relational database model, and propose extensions that are specialized to handling preference data. Specifically, we introduce a special type of a relation that is designed for preference data, and describe composable operators on preference relations that can be embedded in SQL statements, for convenient reuse across applications.

In an object-to-relational mapping system (ORM), mapping expressions explain how to expose relational data as objects and how to store objects in tables. If mappings are sufficiently expressive, then it is possible to define lossy mappings. If a user updates an object, stores it in the database based on a lossy mapping, and then retrieves the object from the database, the user might get a different result than the updated state of the object; that is, the mapping might not "roundtrip." To avoid this, the ORM should validate that user-defined mappings roundtrip the data. However, this problem is NP-hard, so mapping validation can be very slow for large or complex mappings.

An important means of allowing non-expert end-users to pose ad hoc queries whether over single databases or data integration systems is through keyword search. Given a set of keywords, the query processor finds matches across different tuples and tables. It computes and executes a set of relational sub-queries whose results are combined to produce the k highest ranking answers. Work on keyword search primarily focuses on single-database, single-query settings: each query is answered in isolation, despite possible overlap between queries posed by different users or at different times; and the number of relevant tables is assumed to be small, meaning that sub-queries can be processed without using cost-based methods to combine work. As we apply keyword search to support ad hoc data integration queries over scientific or other databases on the Web, we must reuse and combine computation. In this paper, we propose an architecture that continuously receives sets of ranked keyword queries, and seeks to reuse work across these queries. We extend multiple query optimization and continuous query techniques, and develop a new query plan scheduling module we call the ATC (based on its analogy to an air traffic controller). The ATC manages the flow of tuples among a multitude of pipelined operators, minimizing the work needed to return the top-k answers for all queries. We also develop techniques to manage the sharing and reuse of state as queries complete and input data streams are exhausted. We show the effectiveness of our techniques in handling queries over real and synthetic data sets.

To enable smart environments and self-tuning data centers, we are developing the Aspen system for integrating physical sensor data, as well as stream data coming from machine logical state, and database or Web data from the Internet. A key component of this system is a query processor optimized for limited-bandwidth, possibly battery-powered devices with multiple hop wireless radio communications. This query processor is given a portion of a data integration query, possibly including joins among sensors, to execute. Several recent papers have developed techniques for computing joins in sensors, but these techniques are static and are only appropriate for specific join selectivity ratios. We consider the problem of dynamic join optimization for sensor networks, developing solutions that employ cost modeling, as well as adaptive learning and self-tuning heuristics to choose the best algorithm under real and variable selectivity values. We focus on in-network join computation, but our architecture extends to other approaches (and we compare against these). We develop basic techniques assuming selectivities are uniform and known in advance, and optimization can be done on a pairwise basis; we then extend the work to handle joins between multiple pairs, when selectivities are not fully known. We experimentally validate our work at scale using standard datasets.

demonstration SmartCIS: integrating digital and physical environments Share on Authors: Mengmeng Liu University of Pennsylvania, Philadelphia, PA, USA University of Pennsylvania, Philadelphia, PA, USAView Profile , Svilen R. Mihaylov University of Pennsylvania, Philadelphia, PA, USA University of Pennsylvania, Philadelphia, PA, USAView Profile , Zhuowei Bao University of Pennsylvania, Philadelphia, PA, USA University of Pennsylvania, Philadelphia, PA, USAView Profile , Marie Jacob University of Pennsylvania, Philadelphia, PA, USA University of Pennsylvania, Philadelphia, PA, USAView Profile , Zachary G. Ives University of Pennsylvania, Philadelphia, PA, USA University of Pennsylvania, Philadelphia, PA, USAView Profile , Boon Thau Loo University of Pennsylvania, Philadelphia, PA, USA University of Pennsylvania, Philadelphia, PA, USAView Profile , Sudipto Guha University of Pennsylvania, Philadelphia, PA, USA University of Pennsylvania, Philadelphia, PA, USAView Profile Authors Info & Claims SIGMOD '09: Proceedings of the 2009 ACM SIGMOD International Conference on Management of dataJune 2009 Pages 1111–1114https://doi.org/10.1145/1559845.1559996Online:29 June 2009Publication History 6citation263DownloadsMetricsTotal Citations6Total Downloads263Last 12 Months2Last 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

The number of potentially-related data resources available for querying --- databases, data warehouses, virtual integrated schemas --- continues to grow rapidly. Perhaps no area has seen this problem as acutely as the life sciences, where hundreds of large, complex, interlinked data resources are available on fields like proteomics, genomics, disease studies, and pharmacology. The schemas of individual databases are often large on their own, but users also need to pose queries across multiple sources, exploiting foreign keys and schema mappings. Since the users are not experts, they typically rely on the existence of pre-defined Web forms and associated query templates, developed by programmers to meet the particular scientists' needs. Unfortunately, such forms are scarce commodities, often limited to a single database, and mismatched with biologists' information needs that are often context-sensitive and span multiple databases. We present a system with which a non-expert user can author new query templates and Web forms, to be reused by anyone with related information needs. The user poses keyword queries that are matched against source relations and their attributes; the system uses sequences of associations (e.g., foreign keys, links, schema mappings, synonyms, and taxonomies) to create multiple ranked queries linking the matches to keywords; the set of queries is attached to a Web query form. Now the user and his or her associates may pose specific queries by filling in parameters in the form. Importantly, the answers to this query are ranked and annotated with data provenance, and the user provides feedback on the utility of the answers, from which the system ultimately learns to assign costs to sources and associations according to the user's specific information need, as a result changing the ranking of the queries used to generate results. We evaluate the effectiveness of our method against "gold standard" costs from domain experts and demonstrate the method's scalability.