Systems, methods, and devices for a cyberphysical (IoT) software application development platform based upon a model driven architecture and derivative IoT SaaS applications are disclosed herein. The system may include a time-series data component to receive time-series data from time-series data sources. The system may include a relational data component to receive relational data from relational data sources. The system may include a persistence component to store the time-series data in a key-value store and store the relational data in a relational database. The system may include a data services component to extract, transform, and load aggregate data into a multi-dimensional data store; implement a type layer over a plurality of data stores comprising the key-value store, the relational database, and the multi-dimensional data store. The data services component may include definitions for a plurality of types based on the plurality of data stores.

Systems, methods, and devices for an enterprise internet-of-things application development platform

Prior work has shown dramatic acceleration for various database operations on GPUs, but only using primitives that are not part of conventional database languages such as SQL. This paper implements a subset of the SQLite command processor directly on the GPU. This dramatically reduces the effort required to achieve GPU acceleration by avoiding the need for database programmers to use new programming languages such as CUDA or modify their programs to use non-SQL libraries. This paper focuses on accelerating SELECT queries and describes the considerations in an efficient GPU implementation of the SQLite command processor. Results on an NVIDIA Tesla C1060 achieve speedups of 20-70X depending on the size of the result set.

/pdf/accelerating-sql-database-operations-on-a-gpu-with-cuda-5ckiz7v0i4.pdf

Accelerating SQL database operations on a GPU with CUDA

System, method and computer program product for processing a query are disclosed. Query processing includes partitioning the stored data into a plurality of partitions based on at least one vertex in the plurality of vertexes, storing at least another triple in the plurality of triples on the at least one node, assigning, based on the triple containing the at least one vertex, at least one partition in the plurality of partitions corresponding to the triple to at least one node in the plurality of nodes, and processing, based on the assigning, the query by processing the plurality of partitions.

Query Execution Systems and Methods

Large-scale data processing needs of enterprises today are primarily met with distributed and parallel computing in data centers. MapReduce has emerged as an important programming model for these environments. Since today's data centers run many MapReduce jobs in parallel, it is important to find a good scheduling algorithm that can optimize the completion times of these jobs. While several recent papers focused on optimizing the scheduler, there exists very little theoretical understanding of the scheduling problem in the context of MapReduce. In this paper, we seek to address this problem by first presenting a simplified abstraction of the MapReduce scheduling problem, and then formulate the scheduling problem as an optimization problem.We devise various online and offline algorithms to arrive at a good ordering of jobs to minimize the overall job completion times. Since optimal solutions are hard to compute (NP-hard), we propose approximation algorithms that work within a factor of 3 of the optimal. Using simulations, we also compare our online algorithm with standard scheduling strategies such as FIFO, Shortest Job First and show that our algorithm consistently outperforms these across different job distributions.

Scheduling in mapreduce-like systems for fast completion time

Users of MapReduce often run into performance problems when they scale up their workloads. Many of the problems they encounter can be overcome by applying techniques learned from over three decades of research on parallel DBMSs. However, translating these techniques to a Map-Reduce implementation such as Hadoop presents unique challenges that can lead to new design choices. This paper describes how column-oriented storage techniques can be incorporated in Hadoop in a way that preserves its popular programming APIs.We show that simply using binary storage formats in Hadoop can provide a 3x performance boost over the naive use of text files. We then introduce a column-oriented storage format that is compatible with the replication and scheduling constraints of Hadoop and show that it can speed up MapReduce jobs on real workloads by an order of magnitude. We also show that dealing with complex column types such as arrays, maps, and nested records, which are common in MapReduce jobs, can incur significant CPU overhead. Finally, we introduce a novel skip list column format and lazy record construction strategy that avoids deserializing unwanted records to provide an additional 1.5x performance boost. Experiments on a real intranet crawl are used to show that our column-oriented storage techniques can improve the performance of the map phase in Hadoop by as much as two orders of magnitude.

/pdf/column-oriented-storage-techniques-for-mapreduce-3yjhe8idap.pdf

Column-oriented storage techniques for MapReduce

One embodiment is a method that uses MapReduce and Relation Valued Functions (RVFs) with parallel processing to search a database and obtain search results.

Map-reduce and parallel processing in databases

While parallel DBMSs do support large scale parallel query processing on partitioned data, the reach of more general applications relies on User Defined Functions (UDFs). However, the existent UDF technology is insufficient both conceptually and practically. A UDF is not a relation-in, relation-out operator, which restricts its ability to model complex applications defined on a set of tuples rather than on a single one, and to be composed with other relational operators in a query. Further, to interact with the query execution efficiently, a UDF must be coded with complex interactions with DBMS internal data structures and system calls which is often beyond the expertise of an analytics application developer.To solve these problems, we start with wrapping general applications with Relation Valued Functions (RVFs); then based on the notion of invocation patterns, we provide focused system support for efficiently integrating RVF execution into the query processing pipeline. We further distinguish the system responsibility and the user responsibility in RVF development, by separating an RVF into the RVF-Shell for dealing with system interaction, and the user-function for pure application logic, such that the RVF-Shell can be constructed in terms of high-level APIs. These mechanisms enable us to solve the essential problems in supporting MapReduce and other analytics computation models inside a parallel database engine: modeling complex applications, integrating them into query processing, and shielding analytics developers from DBMS internal details.Prototyped on a commercial and proprietary parallel database engine, our experience reveals the practical value of the proposed approaches.

Efficiently support MapReduce-like computation models inside parallel DBMS

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Map-reduce and parallel processing in databases

Efficiently support MapReduce-like computation models inside parallel DBMS