VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Using SQL to Build New Aggregates and Extenders for Object- Relational Systems
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
User-defined aggregate functions: bridging theory and practice
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Interpreting the data: Parallel analysis with Sawzall
Scientific Programming - Dynamic Grids and Worldwide Computing
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Pig latin: a not-so-foreign language for data processing
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
SCOPE: easy and efficient parallel processing of massive data sets
Proceedings of the VLDB Endowment
A comparison of approaches to large-scale data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Distributed data-parallel computing using a high-level programming language
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Distributed aggregation for data-parallel computing: interfaces and implementations
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
Proceedings of the VLDB Endowment
MAD skills: new analysis practices for big data
Proceedings of the VLDB Endowment
Hive: a warehousing solution over a map-reduce framework
Proceedings of the VLDB Endowment
The DataPath system: a data-centric analytic processing engine for large data warehouses
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Dremel: interactive analysis of web-scale datasets
Proceedings of the VLDB Endowment
Predictive analytics with surveillance big data
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
Scalable I/O-bound parallel incremental gradient descent for big data analytics in GLADE
Proceedings of the Second Workshop on Data Analytics in the Cloud
Sampling estimators for parallel online aggregation
BNCOD'13 Proceedings of the 29th British National conference on Big Data
| Hi-index | 0.00 |
In this paper we introduce GLADE, a scalable distributed framework for large scale data analytics. GLADE consists of a simple user-interface to define Generalized Linear Aggregates (GLA), the fundamental abstraction at the core of GLADE, and a distributed runtime environment that executes GLAs by using parallelism extensively. GLAs are derived from User-Defined Aggregates (UDA), a relational database extension that allows the user to add specialized aggregates to be executed inside the query processor. GLAs extend the UDA interface with methods to Serialize/Deserialize the state of the aggregate required for distributed computation. As a significant departure from UDAs which can be invoked only through SQL, GLAs give the user direct access to the state of the aggregate, thus allowing for the computation of significantly more complex aggregate functions. GLADE runtime is an execution engine optimized for the GLA computation. The runtime takes the user-defined GLA code, compiles it inside the engine, and executes it right near the data by taking advantage of parallelism both inside a single machine as well as across a cluster of computers. This results in maximum possible execution time performance (all our experimental tasks are I/O-bound) and linear scaleup.