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The generation and collection of big data series are becoming an integral part of many emerging applications in sciences, IoT, finance, and web applications among several others. The terabyte-scale of data series has motivated recent efforts to design fully distributed techniques for supporting operations such as approximate kNN similarity search, which is a building block operation in most analytics services on data series. Unfortunately, these techniques are heavily geared towards achieving scalability at the cost of sacrificing the results' accuracy. State-of-the-art systems report accuracy below 10% and 40%, respectively, which is not practical for many real-world applications. In this paper, we investigate the root problems in these existing techniques that limit their ability to achieve better a trade-off between scalability and accuracy. Then, we propose a framework, called CLIMBER, that encompasses a novel feature extraction mechanism, indexing scheme, and query processing algorithms for supporting approximate similarity search in big data series. For CLIMBER, we propose a new loss-resistant dual representation composed of rank-sensitive and ranking-insensitive signatures capturing data series objects. Based on this representation, we devise a distributed two-level index structure supported by an efficient data partitioning scheme. Our similarity metrics tailored for this dual representation enables meaningful comparison and distance evaluation between the rank-sensitive and ranking-insensitive signatures. Finally, we propose two efficient query processing algorithms, CLIMBER-kNN and CLIMBER-kNN-Adaptive, for answering approximate kNN similarity queries. Our experimental study on real-world and benchmark datasets demonstrates that CLIMBER, unlike existing techniques, features results' accuracy above 80% while retaining the desired scalability to terabytes of data., Comment: 16 pages, 14 figures, 1 table

Organizations are collecting increasingly large amounts of data for data driven decision making. These data are often dumped into a centralized repository, e.g., a data lake, consisting of thousands of structured and unstructured datasets. Perversely, such mixture of datasets makes the problem of discovering elements (e.g., tables or documents) that are relevant to a user's query or an analytical task very challenging. Despite the recent efforts in data discovery, the problem remains widely open especially in the two fronts of (1) discovering relationships and relatedness across structured and unstructured datasets where existing techniques suffer from either scalability, being customized for a specific problem type (e.g., entity matching or data integration), or demolishing the structural properties on its way, and (2) developing a holistic system for integrating various similarity measurements and sketches in an effective way to boost the discovery accuracy. In this paper, we propose a new data discovery system, named CMDL, for addressing these two limitations. CMDL supports the data discovery process over both structured and unstructured data while retaining the structural properties of tables.

Imperfect databases are very common in many applications due to various reasons ranging from data-entry errors, transmission or integration errors, and wrong instruments' readings, to faulty experimental setups leading to incorrect results. The management and query processing of imperfect databases is a very challenging problem as it requires incorporating the data's qualities within the database engine. Even more challenging, the qualities are typically not static and may evolve over time. Unfortunately, most of the state-of-art techniques deal with the data quality problem as an offline task that is in total isolation of the query processing engine (carried out outside the DBMS). Hence, end-users will receive the queries' results with no clue on whether or not the results can be trusted for further analysis and decision making. In this paper, we propose the it "QTrail-DB" system that fundamentally extends the standard DBMSs to support imperfect databases with evolving qualities. QTrail-DB introduces a new quality model based on the new concept of "Quality Trails", which captures the evolution of the data's qualities over time. QTrail-DB extends the relational data model to incorporate the quality trails within the database system. We propose a new query algebra, called "QTrail Algebra", that enables seamless and transparent propagation and derivations of the data's qualities within a query pipeline. As a result, a query's answer will be automatically annotated with quality-related information at the tuple level. QTrail-DB propagation model leverages the thoroughly-studied propagation semantics present in the DB provenance and lineage tracking literature, and thus there is no need for developing a new query optimizer. QTrail-DB is developed within PostgreSQL and experimentally evaluated using real-world datasets to demonstrate its efficiency and practicality., Comment: 17 Pages, 13 Figures

Biologists are increasingly using databases for storing and managing their data. Biological databases typically consist of a mixture of raw data, metadata, sequences, annotations, and related data obtained from various sources. Current database technology lacks several functionalities that are needed by biological databases. In this paper, we introduce bdbms, an extensible prototype database management system for supporting biological data. bdbms extends the functionalities of current DBMSs to include: (1) Annotation and provenance management including storage, indexing, manipulation, and querying of annotation and provenance as first class objects in bdbms, (2) Local dependency tracking to track the dependencies and derivations among data items, (3) Update authorization to support data curation via content-based authorization, in contrast to identity-based authorization, and (4) New access methods and their supporting operators that support pattern matching on various types of compressed biological data types. This paper presents the design of bdbms along with the techniques proposed to support these functionalities including an extension to SQL. We also outline some open issues in building bdbms., Comment: This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but, you must attribute the work to the author and CIDR 2007. 3rd Biennial Conference on Innovative Data Systems Research (CIDR) January 710, 2007, Asilomar, California, USA