Big Data Flow Adjustment Using Knapsack Problem

Process-driven applications evolve rapidly through the interaction between executable BPMN (Business Process Modeling and Notation) models, business tasks, and external services. Given these components operate on some shared process data, it is imperative to recover the latent data by visiting relation, which is known as data flow among these tasks. Data flow will benefit some typical applications including data flow anomaly checking and data privacy protection. However, in most cases, the complete data flow in a business process is not explicitly defined but hidden in model elements such as form declarations, variable declarations, and program code. Some methods to recovering data flow based on process model analysis of source code have some drawbacks; i.e., for security reasons, users do not want to provide source code but only encapsulated methods; therefore, data flows are difficult to analyze. We propose a method to generate running logs that are used to produce a complete data flow picture combined with the static code analysis method. This method combines the simple and easy-to-use characteristics of static code analysis methods and makes up for the shortcomings of static code analysis methods that cannot adapt to complex business processes, and as a result, the analyzed data flow is inaccurate. Moreover, a holistic framework is proposed to generate the data flow graph. The prototype system designed on Camunda and Flowable BPM (business process management) engine proves the applicability of the solution. The effectiveness of our method is validated on the prototype system.

The Constant Murmur of Data

Path planning is key research topic in the field of robotics research, transportation, bioinformatics, virtual prototype designing, gaming, computer aided designs, and virtual reality estimation. In optimal path planning, it is important to determine the collision free optimal and shortest path. There may be various aspects to determine the optimal path based on workspace environment and obstacle types. In this research work, optimal path is determined based on the workspace environment having static obstacles and unknown environment area. A hybrid approach of meta-heuristic algorithm of Bat Algorithm (BA) and Cuckoo Search (CS) is used to determine the optimal path from defined source to destination. For experimentation, case study area of Alwar region, Rajasthan is considered which consist of urban and vegetation area. The reason for the selection of BA and CS for the path planning is the wide application and success of implementation of these concepts in the field of robotics and path planning. The consideration of individual BA for path planning can lead to problem of trapping between local optima. This obligates us to hybridize the concept of BA with some other efficient problem solving concept like CS. The hybridized concept of BA and CS is initially tested with standard benchmarks functions, after that considered for the application of path planning. Results of hybrid path planning concept are compared with individual CS and BA concepts in terms of simulation time and minimum number of iteration required to achieve the optimal path from defined source to destination. The evaluated results comparison of hybrid approach with individual concepts indicates the dominance of proposed hybrid concept in terms of standard benchmarks functions and other parameters as well.

EU-UK Data Flows, Brexit and No-Deal: Adequacy or Disarray?

Path planning is a path used in guiding Unmanned Aerial Vehicles (UAVs), the three dimensional optimal path is calculated by A-star and Dijkstra algorithms proposed in the three dimensional environments. Simulation environments consist of a hypothetical environment and a realistic environment and including static obstacles or the shapes of modern buildings. This paper is to find the shortest path and avert obstacles to from the starting off node to the landing node and finding the shortest optimal path on the network nodes using algorithms proposed through the formation of a three dimensional network by connecting link nodes with adjacent starting off node and landing node so that robots can perform their tasks as quickly possible in the environment. The simulation environment has been established to evaluate the performance of the A-star and Dijkstra algorithms. The results proved that A-star algorithm outperformed Dijkstra’s algorithm in the execution time factor in finding the optimal path only, but they are similar in distance factor and passe nodes in the optimal path in all cases considered.

The article analyzes the existing methods of managing data flows in mobile radio communications. During the analysis, the peculiarities of the functioning and construction of these networks were considered. It has been established that such networks have features different from other networks, such as mobility, dynamic topology, independent organization, use of mobile means of communication, lack of fixed data transmission routes, etc. The task of providing routing in mobile radio communication devices, mobile radio networks, and the types of routing in communication networks are considered. The conditions for the implementation of the routing task and the features that characterize the routing process have been established. The levels of the OSI open systems model and the most well-known protocols that work at these levels and are used in data flow management are considered. Data flow management methods are classified by type and a list of requirements for the process of data flow transmission in mobile radio networks is defined, taking into account the peculiarities of the functioning of mobile radio communication means, which must be taken into account when building a mobile radio network management system. It has been established that at the current stage of information technology development, data flow management methods are mostly adapted for use in computer, wired or fixed networks and do not take into account the conditions of use in mobile radio communication devices. It has been established that the construction and operation of an effective data flow control subsystem in the control systems of mobile radio networks and mobile radio communication means requires the use of the latest technologies and modern approaches in the development of data flow control methods and methods that will ensure the functioning of the corresponding data flow control subsystem. When choosing protocols, methods, methods of managing data flows, it is proposed to focus on adaptation, modification of approaches or their combined use based on the target function, taking into account the peculiarities of the functioning of mobile radio networks and mobile radio communication means.

Resting state functional magnetic resonance imaging (rfMRI) can be used to measure functional connectivity and then identify brain networks and related brain disorders and diseases. To explore these complex networks, however, huge amounts of data are necessary. Recent advances in neuroimaging technologies, and the unique methodological approach of rfMRI, have enabled us to an era of Biomedical Big Data. The recent progress of big data sharing projects with their challenges are discussed. This increasing amount of neuroimaging data has greatly increased the importance of developing preprocessing pipelines and advanced analytic techniques, which are better at handling large-scale datasets. Before applying any analysis method on rfMRI data, several preprocessing steps need to be applied to reduce all unwanted effects. Three alternative ways to get access to big preprocessed rfMRI data are presented involving the minimal preprocessing pipelines. There are several commonly used methods to examine functional connectivity. However, they become limited in the analysis of big data, and a new tool to explore such data is necessary. We propose a number of novel methods rooted in algebraic topology and collectively referred to as Topological Data Analysis to rfMRI functional connectivity. Their properties for big data analysis are also discussed.

Business intelligence process analyzed data and found certain insights so that managers, executives, or higher authorities of the enterprise can take appropriate decision. Business intelligence provides past, current, and future views of the business actions. Big data indicates big amount of data that is growing exponentially with time. Data analytics check the huge amount of data and indicates some insights. The objective of big data analytics is to discover information like hidden patterns, correlations, market performance, and preferences of customer, so organization can make business decision. This paper presents a new methodology for analysis of huge amount of data using an unsupervised technique of machine learning.

There is a huge amount of data creating during the fourth industry revaluation and the data are generating explosively by various fields of the Internet of Things (IoT). The organizations are producing and storing the huge amount of data into the data servers every moment. This data comes from social media, sensors, tracking, website, and online news articles. The Google, Facebook, Walmart, and Taobao are the most remarkable organizations are generating most of the data in the web servers. Data comes into three forms as structured (text/numeric), semi structured (audio, video, and image) and unstructured (XML and RSS feeds). A business makes revenue from the analysis of 20% of such data, which is a structured form while 80% of data is unstructured. Therefore, unstructured data contains valuable information that can help the organization to improve the business productive, better decision-making, extract the insights, new products and services and understand the market conditions in various fields such as shopping, finance, education, manufacturing, and healthcare. The unstructured data are needed to be analyzed and distribute in a structured manner, that is required information’s are to be gathered through the data mining techniques are used to mining the data. In this paper, expose the importance of data analytics and data management for beneficial usage of business intelligence, big data, data mining and machine and data management. In addition, the different techniques that can be used to discover the knowledge and useful information from such data been analyzed. This can be beneficial for numerous users concern on text mining and convert complex data into meaningful information for researchers, analyst, data scientist, and business decision makers as well.

Greedy algorithms for a class of knapsack problems with binary weights

Shuffled frog leaping algorithm and its application to 0/1 knapsack problem

Ambient intelligence is an emerging platform that provides advances in sensors and sensor networks, pervasive computing, and artificial intelligence to capture the real time climate data. This result continuously generates several exabytes of unstructured sensor data and so it is often called big climate data. Nowadays, researchers are trying to use big climate data to monitor and predict the climate change and possible diseases. Traditional data processing techniques and tools are not capable of handling such huge amount of climate data. Hence, there is a need to develop advanced big data architecture for processing the real time climate data. The purpose of this paper is to propose a big data based surveillance system that analyzes spatial climate big data and performs continuous monitoring of correlation between climate change and Dengue. Proposed disease surveillance system has been implemented with the help of Apache Hadoop MapReduce and its supporting tools.

It has been reported for multi-objective knapsack problems that the recombination of similar parents often improves the performance of evolutionary multi-objective optimization (EMO) algorithms. Recently performance improvement was also reported by exchanging only a small number of genes between two parents (i.e., crossover with a very small gene exchange probability) without choosing similar parents. In this paper, we examine these performance improvement schemes through computational experiments where NSGA-II is applied to 500-item knapsack problems with 2-10 objectives. We measure the parent-parent distance and the parent-offspring distance in computational experiments. Clear performance improvement is observed when the parent-offspring distance is small. To further examine this observation, we implement a distance-based crossover operator where the parent-offspring distance is specified as a user-defined parameter. Performance of NSGA-II is examined for various parameter values. Experimental results show that an appropriate parameter value (parent-offspring distance) is surprisingly small. It is also shown that a very small parameter value is beneficial for diversity maintenance.

This paper studies the flexible large-scale supplier selection and order allocation problem with various quantity discounts, i.e., no discount, all-unit discount, incremental discount, and carload discount. It fills a literature gap that models usually formulate one or seldom two types because of the modeling and solution difficulty. All suppliers offering the same discount are far from reality, especially when the number of suppliers is large. The proposed model is a variant of the NP-hard knapsack problem. The greedy algorithm, which solves the fractional knapsack problem optimally, is applied to cope with the challenge. Three greedy algorithms are developed using a problem property and two sorted lists. Simulations show the average optimality gaps are 0.1026%, 0.0547%, and 0.0234% and the model can be solved in centiseconds, densiseconds, and seconds for supplier numbers 1000, 10000, and 100000. This allows the full use of data in the big data era.

Several approaches have addressed the consistency and automatic enactment dimensions of CSCL scripts with data flow, but they have not appropriately tackled the problem of reusing such learning designs. For instance, workflow-based solutions such as LeadFlow4LD only capture particular case behaviors, instead of describing generic data flow situations. This limitation hinders the reusability of these designs because the workflow needs to be adapted for specific technical, teaching and social contexts. This adaptation is complex and time consuming, especially with a large number of students. In order to show the relevance of this problem, this paper analyzes the LeadFlow4LD approach through a real-world complex CSCL script. The study characterizes the reuse effort of CSCL scripts with and without data flow definition, in different social context settings. The findings illustrate how the data flow representation may affect the particularization of complex CSCL scripts, and pave the path for alternative, higher abstraction level representations of data flows, to reduce the reuse effort.