To derive efficient sorting architectures constrained to application-specific input/output conditions, we present in this paper a systematic design methodology that can effectively prune dispensable compare-and-swap (CAS) units. Unlike the previous works resorting to heuristic approaches, the proposed framework exploits the zero-one principle to validate the pruning of a CAS unit at a time, generating the cost-optimized sorter architecture in an iterative manner with a reasonable complexity. In addition to the given input/output constraints, we newly develop the architecture options for the proposed framework, allowing more design spaces for finding the most attractive constrained-sorter design. For 8-list polar decoders, the proposed framework successfully reduces 70% of CAS units in the baseline full sorter, relaxing the area-time complexity by 35% compared with the state-of-the-art solutions.

The proliferation of GPS-enabled mobile devices has generated geo-tagged data at an unprecedented rate over the past decade. Data-processing systems that aim to ingest, store, index, and analyze Big Data must deal with such geo-tagged data efficiently. In this paper, among representative, disk-resident spatial indexing methods that have been adopted by major SQL and NoSQL systems, we implement five variants of these methods in the form of Log-Structured Merge-tree-based (LSM) spatial indexes in order to evaluate their pros and cons for dynamic geo-tagged Big Data. We have implemented the alternatives, including LSM-based B-tree, R-tree, and inverted index variants, in Apache AsterixDB, an open source Big Data management system. This implementation enabled comparison in terms of real end-to-end performance, including logging and locking overheads, in a full-function, query-based system setting. Our evaluation includes both static and dynamic workloads, ranging from a "load once, query many" case to a case where continuous concurrent incremental inserts are mixed with concurrent queries. Based on the results, we discuss the pros and cons of the five index variants.

AsterixDB is a new, full-function BDMS (Big Data Management System) with a feature set that distinguishes it from other platforms in today's open source Big Data ecosystem. Its features make it well-suited to applications like web data warehousing, social data storage and analysis, and other use cases related to Big Data. AsterixDB has a flexible NoSQL style data model; a query language that supports a wide range of queries; a scalable runtime; partitioned, LSM-based data storage and indexing (including B + -tree, R-tree, and text indexes); support for external as well as natively stored data; a rich set of built-in types; support for fuzzy, spatial, and temporal types and queries; a built-in notion of data feeds for ingestion of data; and transaction support akin to that of a NoSQL store. Development of AsterixDB began in 2009 and led to a mid-2013 initial open source release. This paper is the first complete description of the resulting open source AsterixDB system. Covered herein are the system's data model, its query language, and its software architecture. Also included are a summary of the current status of the project and a first glimpse into how AsterixDB performs when compared to alternative technologies, including a parallel relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data analytics platform, for things that both technologies can do. Also included is a brief description of some initial trials that the system has undergone and the lessons learned (and plans laid) based on those early "customer" engagements.

Social networks, online communities, mobile devices, and instant messaging applications generate complex, unstructured data at a high rate, resulting in large volumes of data. This poses new challenges for data management systems that aim to ingest, store, index, and analyze such data efficiently. In response, we released the first public version of AsterixDB, an open-source Big Data Management System (BDMS), in June of 2013. This paper describes the storage management layer of AsterixDB, providing a detailed description of its ingestion-oriented approach to local storage and a set of initial measurements of its ingestion-related performance characteristics. In order to support high frequency insertions, AsterixDB has wholly adopted Log-Structured Merge-trees as the storage technology for all of its index structures. We describe how the AsterixDB software framework enables "LSM-ification" (conversion from an in-place update, disk-based data structure to a deferred-update, append-only data structure) of any kind of index structure that supports certain primitive operations, enabling the index to ingest data efficiently. We also describe how AsterixDB ensures the ACID properties for operations involving multiple heterogeneous LSM-based indexes. Lastly, we highlight the challenges related to managing the resources of a system when many LSM indexes are used concurrently and present AsterixDB's initial solution.

At UC Irvine, we are building a next generation parallel database system, called ASTERIX, as our approach to addressing today's "Big Data" management challenges. ASTERIX aims to combine time-tested principles from parallel database systems with those of the Web-scale computing community, such as fault tolerance for long running jobs. In this demo, we present a whirlwind tour of ASTERIX, highlighting a few of its key features. We will demonstrate examples of our data definition language to model semi-structured data, and examples of interesting queries using our declarative query language. In particular, we will show the capabilities of ASTERIX for answering geo-spatial queries and fuzzy queries, as well as ASTERIX' data feed construct for continuously ingesting data.

Nowadays, due to the increased user requirements of the fast and reliable data management operation for mobile applications, major device vendors use embedded DBMS for their mobile devices such as MP3 players, mobile phones, digital cameras and PDAs. However, database logging is the major bottleneck against the fast response time. There has been a lot of work minimizing logging overhead but no single recovery method provides the best performance to a variety of database workloads. In this paper, we present a novel recovery method called adaptive logging which can switch the logging method from ARIES to shadow paging adaptively at a page level according to the update state of each page on run time. Also, we propose a log compaction method called deferred logging which removes redundant logs by deferring to create log records until the updated data page is flushed or until the transaction commits. Deferred logging is coupled with adaptive logging seamlessly so that it boosts the performance of adaptive logging by reducing the typical overhead of hybrid methods. We have implemented the proposed approaches to our embedded DBMS which was deployed to more than 10 million mobile devices and evaluated them through a real world application on a mobile device. The result shows that our approaches can reduce logging overhead significantly and consequently can improve the response time of both small update transaction and large update transaction effectively.