Real Analytics Research Articles

Analysis of Data Stream Processing At Edge Layer for Internet of Things

The progress of internet of things at a rapid pace and simultaneous development of the technologies and the processing capabilities has paved way for the development of decentralized systems that are relying on cloud services. Though the decentralized systems are founded on cloud complexities still prevail in transferring all the information’s that are been sensed through the IOT devices to the cloud. This because of the huge streams of information’s gathered by certain applications and the expectation to have a timely response, incurring minimized delay, computing energy and enhanced reliability. So this kind of decentralization has led to the development of middle layer between the cloud and the IOT, and was termed as the Edge layer, meaning bringing down the service of the cloud to the user edge. The paper puts forth the analysis of the data stream processing in the edge layer taking in the complexities involved in the computing the data streams of IOT in an edge layer and puts forth the real time analytics in the edge layer to examine the data streams of the internet of things offering a data- driven insight for parking system in the smart cities.

Robolution: Real Time Predictive Analytics for Industrial Robots

Robots have been playing a very important role in our day-to-day lives and will be a necessity in the coming future. Whenever we hear automation, the first thing that strikes our mind is a robot performing the given task. But if a robot fails to do the task, it could cost an individual or corporate a huge financial loss. In this study, we have learned the working of various robots and drawbacks that hold them back. For this work, we did make a study of drives used in the robot and after that applied the machine learning algorithms to predict the classification of whether the robot will function properly or not, based on the data of drive(s).

Coordinates adapted to vector fields III: Real analyticity

Given a finite collection of $C^1$ vector fields on a $C^2$ manifold which span the tangent space at every point, we consider the question of when there is locally a coordinate system in which these vector fields are real analytic. We give necessary and sufficient, coordinate-free conditions for the existence of such a coordinate system. Moreover, we present a quantitative study of these coordinate charts. This is the third part in a three-part series of papers. The first part, joint with Stovall, lay the groundwork for the coordinate system we use in this paper and showed how such coordinate charts can be viewed as scaling maps for sub-Riemannian geometry. The second part dealt with the analogous questions with real analytic replaced by $C^\infty$ and Zygmund spaces.

From Load to Net Energy Forecasting: Short-Term Residential Forecasting for the Blend of Load and PV Behind the Meter

As distribution networks worldwide are experiencing the adoption of residential solar photovoltaic (PV) more than ever, the need for transiting from the concept of load forecasting to net energy forecasting, i.e. predicting the blend of PV and load as a whole, is pressing. While most of the existing literature has focused on load forecasting, this paper, for the first time, contributes to this transition at both single household and low aggregate levels through a comprehensive study. The paper also proposes a multi-input single-output (MISO) model based on an efficient long short-term memory (LSTM) neural network, by which different household energy profiles help provide more accurate forecasts for other households or aggregate energy profile. This technique, indeed, considers the spatial dependencies of households’ profile indirectly. Through this study, the underlying problem of short-term net energy forecasting is compared to load forecasting, and it is shown how the inclusion of PV generation behind the meter could deteriorate forecasting accuracy. Moreover, the impact of the level of granularity associated with smart meter data on the aggregated net energy forecasting is discussed, and it is revealed that the higher resolution data can potentially alleviate the accuracy lost. Furthermore, online LSTM, as opposed to proposed batch learning MISO LSTM, is used as a forecasting tool. The results show online LSTM is more resilient to sudden changes at the single household level, while MISO LSTM is efficient for aggregate level. The proposed framework is conducted on two real Ausgrid and Solar Analytics case studies in Australia.

E-governance applications and usage patterns in developing countries

E-Governance integrates the use and implementation of technology based suites and modules for the usage in the public domain so that the common persons can be able to use this segment without harassment in public offices. The approach of e-governance associates the online services so that the real time analytics and the implementation can be done to the public services without any delay and with higher degree of accuracy. The use of e-governance is quite prominent in assorted streams whereby the citizen identification and the government services are required to be associated so that the higher performance and cumulative recital is there. The paradigm and implementation perspectives of e-governance are required in the citizen based services for governance and services towards the access to government schemes and thereby this manuscript is having the key focus in this segment for the usage patterns of e-governance in the assorted aspects with specific scenarios of developing countries.

SEEN: A Selective Encryption Method to Ensure Confidentiality for Big Sensing Data Streams

Resource constrained sensing devices are being used widely to build and deploy self-organizing wireless sensor networks for a variety of critical applications such as smart cities, smart health, precision agriculture and industrial control systems. Many such devices sense the deployed environment and generate a variety of data and send them to the server for analysis as data streams. A Data Stream Manager (DSM) at the server collects the data streams (often called big data) to perform real time analysis and decision-making for these critical applications. A malicious adversary may access or tamper with the data in transit. One of the challenging tasks in such applications is to assure the trustworthiness of the collected data so that any decisions are made on the processing of correct data. Assuring high data trustworthiness requires that the system satisfies two key security properties: confidentiality and integrity. To ensure the confidentiality of collected data, we need to prevent sensitive information from reaching the wrong people by ensuring that the right people are getting it. Sensed data are always associated with different sensitivity levels based on the sensitivity of emerging applications or the sensed data types or the sensing devices. For example, a temperature in a precision agriculture application may not be as sensitive as monitored data in smart health. Providing multilevel data confidentiality along with data integrity for big sensing data streams in the context of near real time analytics is a challenging problem. In this paper, we propose a Selective Encryption (SEEN) method to secure big sensing data streams that satisfies the desired multiple levels of confidentiality and data integrity. Our method is based on two key concepts: common shared keys that are initialized and updated by DSM without requiring retransmission, and a seamless key refreshment process without interrupting the data stream encryption/decryption. Theoretical analyses and experimental results of our SEEN method show that it can significantly improve the efficiency and buffer usage at DSM without compromising the confidentiality and integrity of the data streams.

Checking real analyticity on surfaces

We prove that a real-valued function (that is not assumed to be continuous) on a real analytic manifold is analytic whenever all its restrictions to analytic submanifolds homeomorphic to S2 are analytic. This is a real analog for the classical theorem of Hartogs that a function on a complex manifold is complex analytic iff it is complex analytic when restricted to any complex curve.

Re-parameterizing and reducing families of normal operators

We present a new proof of results of Kurdyka & Paunescu, and of Rainer, about real-analytic multi-parameters generalizations of classical results by Rellich and Kato about the reduction in families of univariate deformations of normal operators over real or complex vector spaces of finite dimensions. Given a real analytic family of normal operators over a finite dimensional real or complex vector space, there exists a locally finite composition of blowings-up with smooth centers re-parameterizing the given family such that at each point of the source space of the re-parameterizing mapping, there exists a neighbourhood of any given point over which exists a real analytic orthonormal frame in which the pull back of the operator is in reduced form at every point of the neighbourhood. A free by-product of our proof is the local real analyticity of the eigen-values, which in all prior works was a prerequisite step to get local regular reducing bases.

Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression

A distributed data stream processing system handles real-time, changeable and sudden streaming data load. Its elastic resource allocation has become a fundamental and challenging problem with a fixed strategy that will result in waste of resources or a reduction in QoS (quality of service). Spark Streaming as an emerging system has been developed to process real time stream data analytics by using micro-batch approach. In this paper, first, we propose an improved SVR (support vector regression) based stream data load prediction scheme. Then, we design a spark-based maximum sustainable throughput of time window (MSTW) performance model to find the optimized number of virtual machines. Finally, we present a resource scaling algorithm TWRES (time window resource elasticity scaling algorithm) with MSTW constraint and streaming data load prediction. The evaluation results show that TWRES could improve resource utilization and mitigate SLA (service level agreement) violation.

Pre-processing approaches for imbalanced distributions in regression

Imbalanced domains are an important problem frequently arising in real world predictive analytics. A significant body of research has addressed imbalanced distributions in classification tasks, where the target variable is nominal. In the context of regression tasks, where the target variable is continuous, imbalanced distributions of the target variable also raise several challenges to learning algorithms. Imbalanced domains are characterized by: (1) a higher relevance being assigned to the performance on a subset of the target variable values; and (2) these most relevant values being underrepresented on the available data set. Recently, some proposals were made to address the problem of imbalanced distributions in regression. Still, this remains a scarcely explored issue with few existing solutions. This paper describes three new approaches for tackling the problem of imbalanced distributions in regression tasks. We propose the adaptation to regression tasks of random over-sampling and introduction of Gaussian Noise, and we present a new method called WEighted Relevance-based Combination Strategy (WERCS). An extensive set of experiments provides empirical evidence of the advantage of using the proposed strategies and, in particular, the WERCS method. We analyze the impact of different data characteristics in the performance of the methods. A data repository with 15 imbalanced regression data sets is also provided to the research community.

Conversations and connections: improving real-time health data on behalf of public interest

Real time data and big data analytics is at the forefront and future of healthcare. Current trends indicate a growing interest and commitment to building real time data applications and big data enterprise. This movement must be accompanied by support, commitment and thorough collaboration. Data that proves valuable must drive program implementation, scaled initiatives and effective research. Predictive modeling for surveillance, public health and clinical use must prove cost effective as well. Support and advancement for this must be embraced by all in healthcare, with initial use and incorporation into everyday applications. Additionally, global public health can be aided and should embrace real time data system development, for transparency, predictive analytics and robust surveillance. When aggregated and applied reliably, big data can benefit all stakeholders and the general public. This opportunity should be enhanced, realized and cultivated.

Real Analyticity of Hausdorff Dimension Function of Disconnected Julia Sets of Parabolic Polynomials fa,b(z) = z(1 – az – bz<sup>2</sup>)

Abstract not available
 GANIT J. Bangladesh Math. Soc.Vol. 38 (2018) 119-125

Adaptive Scheduling Parallel Jobs with Dynamic Batching in Spark Streaming

Today enterprises have massive stream data that require to be processed in real time due to data explosion in recent years. Spark Streaming as an emerging system is developed to process real time stream data analytics by using micro-batch approach. The unified programming model of Spark Steaming leads to some unique benefits over other traditional streaming systems, such as fast recovery from failures, better load balancing and resource usage. It treats the continuous stream as a series of micro-batches of data and continuously process these micro-batch jobs. However, efficient scheduling of micro-batch jobs to achieve high throughput and low latency is very challenging due to the complex data dependency and dynamism inherent in streaming workloads. In this paper, we propose A-scheduler, an adaptive scheduling approach that dynamically schedules parallel micro-batch jobs in Spark Streaming and automatically adjusts scheduling parameters to improve performance and resource efficiency. Specifically, A-scheduler dynamically schedules multiple jobs concurrently using different policies based on their data dependencies and automatically adjusts the level of job parallelism and resource shares among jobs based on workload properties. Furthermore, we integrate dynamic batching technique with A-Scheduler to further improve the overall performance of the customized Spark Streaming system. It relies on an expert fuzzy control mechanism to dynamically adjust the length of each batch interval in response to time-varying streaming workload and system processing rate. We implemented A-scheduler and evaluated it with a real-time security event processing workload. Our experimental results show that A-scheduler with dynamic batching can reduce end-to-end latency by 38 percent and meanwhile improve workload throughput and energy efficiency by 23 and 15 percent, respectively, compared to the default Spark Streaming scheduler.

Coordinates Adapted to Vector Fields: Canonical Coordinates

Given a finite collection of C1 vector fields on aC2 manifold which span the tangent space at every point, we consider the question of when there is locally a coordinate system in which these vector fields have a higher level of smoothness. For example, when is there a coordinate system in which the vector fields are smooth, or real analytic, or have Zygmund regularity of some finite order? We address this question in a quantitative way, which strengthens and generalizes previous works on the quantitative theory of sub-Riemannian (aka Carnot–Caratheodory) geometry due to Nagel, Stein, and Wainger, Tao and Wright, the second author, and others. Furthermore, we provide a diffeomorphism invariant version of these theories. This is the first part in a three part series of papers. In this paper, we study a particular coordinate system adapted to a collection of vector fields (sometimes called canonical coordinates) and present results related to the above questions which are not quite sharp; these results form the backbone of the series. The methods of this paper are based on techniques from ODEs. In the second paper, we use additional methods from PDEs to obtain the sharp results. In the third paper, we prove results concerning real analyticity and use methods from ODEs.

MICROLOCAL REGULARITY FOR MIZOHATA TYPE DIFFERENTIAL OPERATORS

We investigate interesting connections between Mizohata type vector fields and microlocal regularity of nonlinear first-order PDEs, establishing results in Denjoy–Carleman classes and real analyticity results in the linear case.

Real analyticity for random dynamics of transcendental functions

Analyticity results of expected pressure and invariant densities in the context of random dynamics of transcendental functions are established. These are obtained by a refinement of work by Rugh [On the dimension of conformal repellors, randomness and parameter dependency. Ann. of Math. (2) 168(3) (2008), 695–748] leading to a simple approach to analyticity. We work under very mild dynamical assumptions. Just the iterates of the Perron–Frobenius operator are assumed to converge. We also provide Bowen’s formula expressing the almost sure Hausdorff dimension of the radial fiberwise Julia sets in terms of the zero of an expected pressure function. Our main application establishes real analyticity for the variation of this dimension for suitable hyperbolic random systems of entire or meromorphic functions.

An “EPIIC” Vision to Evolve Project Integration, Innovation, and Collaboration with Broad Impact for How NASA Executes Complex Projects

Evolving Project Integration, Innovation, and Collaboration (EPIIC) is a vision defined to transform the way projects manage information to support real-time decisions, capture best practices and lessons learned, perform assessments, and manage risk across a portfolio of projects. The foundational project management needs for data and information will be revolutionized through innovations on how we manage and access that data, implement configuration control, and certify compliance. The embedded intelligence of new interactive data interfaces integrate technical and programmatic data such that near real time analytics can be accomplished to more efficiently and accurately complete systems engineering and project management tasks. The system-wide data analytics that are integrated into customized data interfaces allows the growing team of engineers and managers required to develop and implement major NASA missions the ability to access authoritative source(s) of system information while greatly reducing the labor required to complete system assessments. This would allow, for example, much of what is accomplished in a scheduled design review to take place as needed, between any team members, at any time. An intelligent data interface that rigorously integrates systems engineering and project management information in near real time can provide substantially greater insight for systems engineers, project managers, and the large diverse teams required to complete a complex project. System engineers, programmatic personnel (those who focus on cost, schedule, and risk), the technical engineering disciplines, and project management can realize immediate benefit from the shared vision described herein. Implementation of the vision also enables significant improvements in the performance of the engineered system being developed.

One-Sided Extendability and p-Continuous Analytic Capacities

Using complex methods combined with Baire’s Theorem, we show that one-sided extendability, extendability, and real analyticity are rare phenomena on various spaces of functions in the topological sense. These considerations led us to introduce the p-continuous analytic capacity and variants of it, pin {0,1,2, ldots }cup {infty }, for compact or closed sets in mathbb {C}. We use these capacities in order to characterize the removability of singularities of functions in the spaces A^p .

Digital health: a new dimension in rheumatology patient care.

The new digital health innovations have opened up several opportunities to help the clinicians, patients and other caregivers of rheumatology healthcare system in maximizing efficiencies resulting in better patient outcomes. In the global context, digital health technology has the potential to bridge the distance gap between all the key stakeholders involved in rheumatology health care. In this review, we update on the recent advances in the field of digital health and highlight unique features of these technologies which would help in routine care. Application of technology in any form to enable, facilitate or enhance the quality of care is the foundation of digitised care. The components could be smartphone apps, sensors, video, social media platforms or messenger platforms, wearables or a combination of these enabling healthcare delivery and overcoming the constraints of distance, location and time. Digital therapeutics have started evolving and an important step in this direction is the involvement of FDA in the approval process. Speciality specific apps, personalised patient education as per disease status, remote specialist consultations or virtual health coach to guide on lifestyle modifications are some of the developments which have been facilitated by increased digitization in all walks of life. Assisted care with the help of robots rendering care in the hospitals or an intelligent robot guiding a patient by voice and visual sense at home are already at the threshold of entering the mainstream of patient care. Wearable devices equipped with powerful sensors are coming handy in keeping a watch on patient symptoms all the time and providing useful insights on disease progression, clinical response or complications. In chronic care such as rheumatology the implications, possibilities and benefits seem unprecedented. Real time data analytics and artificial intelligence are helping the clinicians, healthcare systems and policy makers optimise the resources and improve patient outcomes. Digitization of healthcare has gained momentum in the recent years and it is envisaged that it could be a catalyst to change, bridge the quality of care and most important democratise the healthcare access across the globe. However, more data, efficacy and objective results are needed which would be fulfilled by ongoing observational studies, clinical trials, systematic review and meta-analysis to further establish the role of digital health in the realms of patient care.

Space-Time Analytic Smoothing Effect for Global Solutions to a System of Nonlinear Schrödinger Equations with Large Data

We study the Cauchy problem for a quadratic system of nonlinear Schrodinger equations in $$L^2$$ -setting with the space dimension $$n=1,2$$ or 3. Recently, the author showed that the local solution for the system of nonlinear Schrodinger equations has space-time analytic smoothing effect for data with exponentially weighted $$L^2$$ -norm. Also as is well known, the quadratic nonlinear Schrodinger equations have global solutions in $$L^2$$ -subcritical setting. Our main purpose of this study is to show real analyticity in both space and time variables of the unique global solution with data which has large exponentially weighted $$L^2$$ -norm.