Streaming Algorithms and Parameterized Streaming

Over the last few years, there has been considerable amount of study and work on developing algorithms for processing massive graphs in the data stream model. Storing massive graphs in the memory of a single machine is not practical which is what the motivation behind data stream algorithms. To obtain space and time efficient algorithms, we develop streaming/semi-streaming algorithms where it is reasonable to assume that the input graph arrives as a stream of edges. We can process the input in either one or multiple passes and the working memory space is restricted

Big data velocity management-from stream to warehouse via high performance memory optimized index join

Efficient resource optimization is critical to manage the velocity and volume of real-time streaming data in near-real-time data warehousing and business intelligence. This article presents a memory optimisation algorithm for rapidly joining streaming data with persistent master data in order to reduce data latency. Typically during the transformation phase of ETL (Extraction, Transformation, and Loading) a stream of transactional data needs to be joined with master data stored on disk. To implement this process, a semi-stream join operator is commonly used. Most semi-stream join operators cache frequent parts of the master data to improve their performance, this process requires careful distribution of allocated memory among the components of the join operator. This article presents a cache inequality approach to optimise cache size and memory. To test this approach, we present a novel Memory Optimal Index-based Join (MOIJ) algorithm. MOIJ supports many-to-many types of joins and adapts to dynamic streaming data. We also present a cost model for MOIJ and compare the performance with existing algorithms empirically as well as analytically. We envisage the enhanced ability of processing near-real-time streaming data using minimal memory will reduce latency in processing big data and will contribute to the development of highperformance real-time business intelligence systems

An efficient closed frequent itemset miner for the MOA stream mining system

Mining itemsets is a central task in data mining, both in the batch and the streaming paradigms. While robust, efficient, and well-tested implementations exist for batch mining, hardly any publicly available equivalent exists for the streaming scenario. The lack of an efficient, usable tool for the task hinders its use by practitioners and makes it difficult to assess new research in the area. To alleviate this situation, we review the algorithms described in the literature, and implement and evaluate the IncMine algorithm by Cheng, Ke, and Ng (2008) for mining frequent closed itemsets from data streams. Our implementation works on top of the MOA (Massive Online Analysis) stream mining framework to ease its use and integration with other stream mining tasks. We provide a PAC-style rigorous analysis of the quality of the output of IncMine as a function of its parameters; this type of analysis is rare in pattern mining algorithms. As a by-product, the analysis shows how one of the user-provided parameters in the original description can be removed entirely while retaining the performance guarantees. Finally, we experimentally confirm both on synthetic and real data the excellent performance of the algorithm, as reported in the original paper, and its ability to handle concept drift.Postprint (published version

A Biologically Motivated Software Retina for Robotic Sensors for ARM-Based Mobile Platform Technology

A key issue in designing robotics systems is the cost of an integrated camera sensor that meets the bandwidth/processing requirement for many advanced robotics applications, especially lightweight robotics applications, such as visual surveillance or SLAM in autonomous aerial vehicles. There is currently much work going on to adapt smartphones to provide complete robot vision systems, as the smartphone is so exquisitely integrated by having camera(s), inertial sensing, sound I/O and excellent wireless connectivity. Mass market production makes this a very low-cost platform and manufacturers from quadrotor drone suppliers to children’s toys, such as the Meccanoid robot [5], employ a smartphone to provide a vision system/control system [7,8]. Accordingly, many research groups are attempting to optimise image analysis, computer vision and machine learning libraries for the smartphone platform. However current approaches to robot vision remain highly demanding for mobile processors such as the ARM, and while a number of algorithms have been developed, these are very stripped down, i.e. highly compromised in function or performance. For example, the semi-dense visual odometry implementation of [1] operates on images of only 320x240pixels. In our research we have been developing biologically motivated foveated vision algorithms based on a model of the mammalian retina [2], potentially 100 times more efficient than their conventional counterparts. Accordingly, vision systems based on the foveated architectures found in mammals have also the potential to reduce bandwidth and processing requirements by about x100 - it has been estimated that our brains would weigh ~60Kg if we were to process all our visual input at uniform high resolution. We have reported a foveated visual architecture [2,3,4] that implements a functional model of the retina-visual cortex to produce feature vectors that can be matched/classified using conventional methods, or indeed could be adapted to employ Deep Convolutional Neural Nets for the classification/interpretation stage. Given the above processing/bandwidth limitations, a viable way forward would be to perform off-line learning and implement the forward recognition path on the mobile platform, returning simple object labels, or sparse hierarchical feature symbols, and gaze control commands to the host robot vision system and controller. We are now at the early stages of investigating how best to port our foveated architecture onto an ARM-based smartphone platform. To achieve the required levels of performance we propose to port and optimise our retina model to the mobile ARM processor architecture in conjunction with their integrated GPUs. We will then be in the position to provide a foveated smart vision system on a smartphone with the advantage of processing speed gains and bandwidth optimisations. Our approach will be to develop efficient parallelising compilers and perhaps propose new processor architectural features to support this approach to computer vision, e.g. efficient processing of hexagonally sampled foveated images. Our current goal is to have a foveated system running in real-time on at least a 1080p input video stream to serve as a front-end robot sensor for tasks such as general purpose object recognition and reliable dense SLAM using a commercial off-the-shelf smartphone. Initially this system would communicate a symbol stream to conventional hardware performing back-end visual classification/interpretation, although simple object detection and recognition tasks should be possible on-board the device. We propose that, as in Nature, foveated vision is the key to achieving the necessary data reduction to be able to implement complete visual recognition and learning processes on the smartphone itself

Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources

Apache Calcite is a foundational software framework that provides query processing, optimization, and query language support to many popular open-source data processing systems such as Apache Hive, Apache Storm, Apache Flink, Druid, and MapD. Calcite's architecture consists of a modular and extensible query optimizer with hundreds of built-in optimization rules, a query processor capable of processing a variety of query languages, an adapter architecture designed for extensibility, and support for heterogeneous data models and stores (relational, semi-structured, streaming, and geospatial). This flexible, embeddable, and extensible architecture is what makes Calcite an attractive choice for adoption in big-data frameworks. It is an active project that continues to introduce support for the new types of data sources, query languages, and approaches to query processing and optimization.Comment: SIGMOD'1