Data Flow System Research Articles

Brain-computer interface based generative design framework: an empirical multi-domain application exploration based on human-factors and form-generation interactive mechanisms

Human experience in an architectural space is defined as the state of mind that is reflected on their physiological, emotional, and cognitive statuses. Ergonomic data, as an objective manifestation of quantifiable signals generated by the human body during specific spatial perception processes, serves as a vital foundation for spatial evaluation and guidance for optimization. Electroencephalogram (EEG) signals, as quantifiable sensory indicators directly arising from the interaction between individuals and external stimuli, hold substantial potential as a data-driven force and as a means of optimization assessment in the study of generative design. Although existing research has effectively established a unidirectional relationship between EEG and spatial-environment assessment, there is still a notable deficiency in addressing the creation of a two-way, mutually informative feedback mechanism. This study investigates the applicability of EEG signals as a data-driven basis for generative design across universal methods. It delves into various scales and scenarios of digital design, from the microscopic to the macroscopic, encompassing planar and volumetric visual elements, the design of architectural spatial environmental characteristics, and urban space design grounded in human perceptual sightlines. The research examines the viability and appropriateness of an interactive generative design method based on form generation, predicated on human-factor physiological data exemplified by EEG signals. This paper initially conducts a methodological and tool-based examination of current research in ergonomics-driven design and the use of EEG for design assessment, thereby discussing the objective feasibility of employing EEG in interactive generative design. Subsequently, the study establishes an integrated data flow system encompassing multiple hardware and software components to form a comprehensive workflow. Following this setup, empirical studies based on this method are conducted at different scales of application, yielding corresponding form-generative outcomes. Finally, this paper substantiates the rationality and feasibility of this framework in multi environment design domains.

Sistem Informasi Reporting Sales And Marketing Berbasis Web (Studi Kasus Amaroossa Hotels Indonesia)

Marketing Sales Department is one of the most important departments in Amaroossa Hotel Indonesia. The purpose of this research is to build a data processing information system of reports from sales and marketing where the source of data from sales and marketing reports be it daily, weekly, monthly reports, and so forth. The application development method uses the waterfall method which starts from the requirements analysis, system and software design, system implementation, and system testing. Whereas for system design using Data Flow Diagrams (DFD) and system development methods are built using Visual Studio Code for coding purposes using the PHP programming language by applying laravel framework. The results of this study produce a website-based system of management of report data from sales and marketing, where the system has several features such as file upload, file view, and file download. Testing system testing uses blackbox testing where testing that has been done on the system produces the conclusion that the system is running well.

GeaFlow: A Graph Extended and Accelerated Dataflow System

GeaFlow is a distributed dataflow system optimized for streaming graph processing, and has been widely adopted at Ant Group, serving various scenarios ranging from risk control of financial activities to analytics on social networks and knowledge graphs. It is built on top of a base with full-fledged stateful stream processing capabilities, extended with a series of graph-aware optimizations to address the space explosion and programming complexity issues of conventional join-based approaches. We propose new state backends and streaming operators that facilitate processing on dynamic graph-structured datasets, reducing space consumed by states. We develop a hybrid domain-specific language that embeds Gremlin into SQL, supporting both table and graph abstractions over streaming data. In addition to streaming workloads, GeaFlow is also extensively used for some batch processing jobs. In the largest deployments to date, GeaFlow is able to process tens of millions of events per second and manage hundreds of terabytes of states.

Data flow in living contexts: A systemic analysis for an unsustainable model

Contemporary residential contexts are characterized by new ICT products and services, which collect data about user behaviours. These data are rarely used to develop services for user or community well-being. By contrast, designers typically focus on these goals in contemporary design. An analysis using Systemic Design tools was carried out to understand the type of data flow that is generated by users in contemporary living contexts, to identify the main stakeholders of this data flow, and to highlight the main problems of data collection for the users, the community, and the local area. This study aims to contribute to this growing area of research, highlighting its most important problem which designer must solve to realize the right user-generated data flow system in contemporary living contexts.

The Accounting information system design of Avia Jaya Savings and Loans Cooperative Palu City

Accounting information systems are very important for a financial organization, one of which is a financial information system in cooperatives. Cooperatives that aim for the welfare of their members must pay attention to their accounting information system, so that it does not become an obstacle in the future. The design of an accounting information system in cooperatives is intended to help cooperatives carry out the process of cooperative activities easily. This study aims to design an accounting information system at the Avia Jaya Member Cooperative. This research is a qualitative research with a case study approach which includes system analysis and continued with the design of information systems. The system design method used in this research is the System Development Life Cycle (SDLC) method or the system development life cycle with the waterfall model. The design of information systems is made using flowcharts, Data Flow Diagrams and system implementation. The results of this study based on the system design using Microsoft Excel based on Visual Basic for Applications at the Avia Jaya employee cooperative, it was found that there was a process in the recording of deposit and loan transactions, as well as the resulting reports. The process of making an accounting information system is carried out based on the results of a case study by analyzing the Avia Jaya Employee Cooperative which does not yet have a system so that it hinders the process of running the accounting information system in the cooperative. The existence of this application design can make it easier for cooperative members to carry out the process of preparing financial reports which previously recorded manually into digital records and make it easier for users to make decisions.

Towards operational phytoplankton recognition with automated high-throughput imaging, near-real-time data processing, and convolutional neural networks

Plankton communities form the basis of aquatic ecosystems and elucidating their role in increasingly important environmental issues is a persistent research question. Recent technological advances in automated microscopic imaging, together with cloud platforms for high-performance computing, have created possibilities for collecting and processing detailed high-frequency data on planktonic communities, opening new horizons for testing core hypotheses in aquatic ecosystems. Analyzing continuous streams of big data calls for development and deployment of novel computer vision and machine learning systems. The implementation of these analysis systems is not always straightforward with regards to operationality, and issues regarding data flows, computing and data treatment need to be considered. We created a data pipeline for automated near-real-time classification of phytoplankton during remote deployment of imaging flow cytometer (Imaging FlowCytobot, IFCB). Convolutional neural network (CNN) is used to classify continuous imaging data with probability thresholds used to filter out images not belonging to our existing classes. The automated data flow and classification system were used to monitor dominating species of filamentous cyanobacteria on the coast of Finland during summer 2021. We demonstrate that good phytoplankton recognition can be achieved with transfer learning utilizing a relatively shallow, publicly available, pre-trained CNN model and fine-tuning it with community-specific phytoplankton images (overall F1-score of 0.95 for test set of our labeled image data complemented with a 50% unclassifiable image portion). This enables both fast training and low computing resource requirements for model deployment making it easy to modify and applicable in wide range of situations. The system performed well when used to classify a natural phytoplankton community over different seasons (overall F1-score 0.82 for our evaluation data set). Furthermore, we address the key challenges of image classification for varying planktonic communities and analyze the practical implications of confused classes. We published our labeled image data set of Baltic Sea phytoplankton community for the training of image recognition models (~63000 images in 50 classes) to accelerate implementation of imaging systems for other brackish and freshwater communities. Our evaluation data set, 59 fully annotated samples of natural communities throughout an annual cycle, is also available for model testing purposes (~150000 images).

The Visualization Approach Based on Data Flow for Traffic Trajectory

To create traffic trajectory visualization through visual graphics interactively, the traffic trajectory-oriented data flow visualization method is proposed. The method uses wizard recommendations to assist in data flow construction for traffic track clustering, correlation analysis and anomaly detection. Firstly, the data flow system is developed to interactively create traffic trajectory visualization. Secondly, the wizard recommendation model based on heterogeneous networks of data flow is proposed to assist in data flow construction. The recommendation model treats wizard recommendations as a graph search problem and feeds user ratings on wizard recommendations to a heterogeneous network of data flow to update node relationships. The results of the case evaluation based on real traffic trajectory data and the evaluation of the Likert scale on users' propensity to choose indicate the validity of the approach.

Transactions across serverless functions leveraging stateful dataflows

Serverless computing is currently the fastest-growing cloud services segment. The most prominent serverless offering is Function-as-a-Service (FaaS), where users write functions and the cloud automates deployment, maintenance, and scalability. Although FaaS is a good fit for executing stateless functions, it does not adequately support stateful constructs like microservices and scalable, low-latency cloud applications. Recently, there have been multiple attempts to add first-class support for state in FaaS systems, such as Microsoft Orleans, Azure Durable Functions, or Beldi. These approaches execute business code inside stateless functions, handing over their state to an external database. In contrast, approaches such as Apache Flink’s StateFun follow a different design: a dataflow system such as Apache Flink handles all state management, messaging, and checkpointing by executing a stateful dataflow graph providing exactly-once state processing guarantees. This design relieves programmers from having to “pollute” their business logic with distributed systems error checking, management, and mitigation. Although programmers can easily develop applications without worrying about messaging and state management, executing transactions across stateful functions remains an open problem.In this paper, we introduce a programming model and implementation for transaction orchestration of stateful serverless functions. Our programming model supports serializable distributed transactions with two-phase commit, as well as eventually consistent workflows with Sagas. We design and implement our programming model on Apache Flink StateFun to leverage Flink’s exactly-once processing and state management guarantees. Our experiments show that the approach of building transactional systems on top of dataflow graphs can achieve very high throughput, but with latency overhead due to checkpointing mechanism guaranteeing the exactly-once processing. We compare our approach to Beldi that implements two-phase commit on AWS lambda functions backed by DynamoDB for state management, as well as an implementation of a system that makes use of CockroachDB as its backend.

Distributed temporal graph analytics with GRADOOP

Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop, its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort.

Designing and building an information system for food distributor companies

Business developments demanded food distributor XYZ Company to change the management of data from manually recording to computerized. Company information data is stored on the computer so that the company can manage data effectively and quickly. The analysis is conducted on the company’s data flow system and is depicted with the Data Flow Diagram. The data object relationships in the company are pointed through the Entity Relationship Diagram. The development of programming applications for companies is done on Windows platforms based on visuals and graphics. Identification of the registration of goods in the company’s old system and presented with images and proposals covering the performance, information, economy, control, efficiency, and service sections of the company’s old systems. The expected result of this research is that companies can obtain a more convenient data recording system, reduce errors in the data recording process. This application is expected to facilitate the reprocessing of sales and purchase data at food distributor companies.

WITHDRAWN: A development architecture of Lora gateway communication technology using artificial intelligence

Since the LoRa is an open standard and supports the sustainable smart cities to develop, which are associated with the circular economy concepts, the researchers are being attracted towards the LoRa (Long Range). Therefore, a development architecture of long range (LoRa) gateway communication technology using artificial intelligence is proposed in this paper. Mysignal development shield is used by IoT devices and sensors for maintenance and remote monitoring. LoRa implements wireless system to transmit physical data to pc or cloud that is gathered by multiple sensors enabled by MySignals. The tasks performed by LoRa are discussed with cloud and IoT server data flow system based on AI. Through LoRa the communication is being implemented with the program of hyper terminal. By getting good results from sensors, the platform of MySignal is developing the proposed architecture. As per outcomes of Simulation clearly shows that many technologies of IoT, complexity reduction, and minimization of delays in the process of decision making at the customer end are supported by LoRa WAN (Wireless Area Network) & hybrid Wi-Fi network. As the deployment cost is less it supports one moveable gateway.

Probeware for the Modern Era: IoT Dataflow System Design for Secondary Classrooms

Sensor systems have the potential to make abstract science phenomena concrete for K-12 students. Internet of Things (IoT) sensor systems provide a variety of benefits for modern classrooms, creating the opportunity for global data production, orienting learners to the opportunities and drawbacks of distributed sensor and control systems, and reducing classroom hardware burden by allowing many students to “listen” to the same data stream. To date, few robust IoT classroom systems have emerged, partially due to lack of appropriate curriculum and student-accessible interfaces, and partially due to lack of classroom-compliant server technology. In this article, we present an architecture and sensor kit system that addresses issues of sensor ubiquity, acquisition clarity, data transparency, reliability, and security. The system has a dataflow programming interface to support both science practices and computational data practices, exposing the movement of data through programs and data files. The IoT Dataflow System supports authentic uses of computational tools for data production through this distributed cloud-based system, overcoming a variety of implementation challenges specific to making programs run for arbitrary duration on a variety of sensors. In practice, this system provides a number of unique yet unexplored educational opportunities. Early results show promise for Dataflow as a valuable learning technology from research conducted in a high school classroom.

Design of a Resilient, High-Throughput, Persistent Storage System for the ATLAS Phase-II DAQ System

The ATLAS experiment will undergo a major upgrade to take advantage of the new conditions provided by the upgraded High-Luminosity LHC. The Trigger and Data Acquisition system (TDAQ) will record data at unprecedented rates: the detectors will be read out at 1 MHz generating around 5 TB/s of data. The Dataflow system (DF), component of TDAQ, introduces a novel design: readout data are buffered on persistent storage while the event filtering system analyses them to select 10000 events per second for a total recorded throughput of around 60 GB/s. This approach allows for decoupling the detector activity from the event selection process. New challenges then arise for DF: design and implement a distributed, reliable, persistent storage system supporting several TB/s of aggregated throughput while providing tens of PB of capacity. In this paper we first describe some of the challenges that DF is facing: data safety with persistent storage limitations, indexing of data at high-granularity in a highly-distributed system, and high-performance management of storage capacity. Then the ongoing R&D to address each of the them is presented and the performance achieved with a working prototype is shown.

Data fusion-based sustainable digital twin system of intelligent detection robotics

In order to give full play to the role of science and technology in achieving the sustainable development goals, digital twin technology based on data fusion plays an irreplaceable role. This paper analyzes the detection process of intelligent detection robots for massage chairs, theoretical research is carried out from two aspects of decision-level fusion and data-level fusion. The principles, levels, and models of information fusion are analyzed, and an in-depth analysis and discussion of DS evidence theory are conducted.Whereas the ambiguity and uncertainty of evaluation indicators in sustainable design, a quantitative model for sustainable design is established in this paper. The entropy weight method is used to determine the weight of sub-indicators, and a decision matrix for the indicators to be evaluated is established. Based on the data detected by the robot while the massage chair is working, the sustainability of the massage chair is evaluated. Reflect the obtained quantitative indicators on the digital twin data flow system, improve the comprehensive performance of products in sustainable design through data fusion, and prove that the necessity of sustainability and the proposed method must be considered in the design and use process feasibility.

Designing Google Form based Information System for Submitting Students’ Final Assignment’s Title

In the study of Industrial Engineering, Kadiri University, the final assignment for the title of three titles will be proposed. The way of proposing the proposal is, when there is a decree from Kaprodi, it will be presented to students who program the final assignment, then there are three plans of title that must be proposed, then after writing three plans of the title, the data will be entered by the library attendant, then it will be listed. After being listed, the title plans are data files that will be addressed to Kaprodi. Then, Kaprodi will decide whice title that must be taken by student as well as who the supervisor is.. Observing those activities, students should come to the campus to register the plans for the titles of the assignment that must be taken. To ease the student, researchers want to design information system that will facilitate the submission of plans for the title of the final assignment in the form of links, the link is derived from Google form. With this ease, students are expected to save time, do not have to come to the campus during the registration of the final assignment title plans. Designing forms and activities, it will be simulated by system flow and data flow diagrams, to know the details of the activities that occur, because the flow of the system will be compared to conventional systems with new one. The methods are assisted by data flow diagrams, system flow, and PIECES method.

Performance evaluation of distributed file systems for the phase-II upgrade of the ATLAS experiment at CERN

Over the next few years, the LHC will prepare for the upcoming High-Luminosity upgrade in which it is expected to deliver ten times more pp collisions. This will create a harsher radiation environment and higher detector occupancy. In this context, the ATLAS experiment, one of the general purpose experiments at the LHC, plans substantial upgrades to the detectors and to the trigger system in order to efficiently select events. Similarly, the Data Acquisition System (DAQ) will have to redesign the data-flow architecture to accommodate for the large increase in event and data rates. The Phase-II DAQ design involves a large distributed storage system that buffers data read out from the detector, while a computing farm (Event Filter) analyzes and selects the most interesting events. This system will have to handle 5.2 TB/s of input data for an event rate of 1 MHz and provide access to 3 TB/s of these data to the filtering farm. A possible implementation for such a design is based on distributed file systems (DFS) which are becoming ubiquitous among the big data industry. Features of DFS such as replication strategies and smart placement policies match the distributed nature and the requirements of the new data-flow system. This paper presents an up-to-date performance evaluation of some of the DFS currently available: GlusterFS, HadoopFS and CephFS. After characterization of the future data-flow systems workload, we report on small-scale raw performance and scalability studies. Finally, we conclude on the suitability of such systems to the tight constraints expected for the ATLAS experiment in phase-II and, in general, what benefits the HEP community can take from these storage technologies.

PERANCANGAN SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN KARIR BERBASIS WEB

This research aims to design a Decision Support System (DSS) to choose a job with web base. So that later this system can be a container consulting user or meember in determining the choice of type of work in accordance with the personality they have. The input data used is the result of personality tests then the data will be processed using Case Based Reasoning (CBR) method to be matched with the psychological value of personality then matched to the type of work then the system will give advice job vacancy according to his personality. The design of this system using structured system design model as data flow and object-based system design is Unified Modeling Language (UML), PHP as programming language with Codeigniter faramework, and MySQL as database. The results of this study is an application that can be used by the community to consult in determining the appropriate job type choices.

Study of Influential Parameters of the Sphere Indentation Used for the Control Function of Material Properties in Forming Operations

The uncertainties of modern, adaptable sheet metal forming systems are classified into model errors and disturbances. To improve the control of production, disturbances in the forming process need to be reduced. For this purpose, a new data flow system was introduced. It connected the data flow of all influencing material parameters into the “material property control function”. To control on-line the forming production line and acquire necessary material data, an indentation test was implemented. The main parameters to follow in this test are pile-up or sink-in values after the embossing of the ball-shaped tool into the material where the innovative approach of fully anisotropic material description was used. To set-up an optimal indentation test, parametric studies were performed with material data of AW 5754-H22. Finite element simulations were used to evaluate the influences of indenter diameter, contact friction and forming history of used the material. Fully anisotropic material behaviour was considered. Novel to this approach were a) the linking of the linear correlation of pile-up with the indentation depth described by gradient k, and b) the linking of gradient k with different pre-strains by a new power function.

Distributed subgraph matching on timely dataflow

Recently there emerge many distributed algorithms that aim at solving subgraph matching at scale. Existing algorithm-level comparisons failed to provide a systematic view of distributed subgraph matching mainly due to the intertwining of strategy and optimization. In this paper, we identify four strategies and three general-purpose optimizations from representative state-of-the-art algorithms. We implement the four strategies with the optimizations based on the common Timely dataflow system for systematic strategy-level comparison. Our implementation covers all representative algorithms. We conduct extensive experiments for both unlabelled matching and labelled matching to analyze the performance of distributed subgraph matching under various settings, which is finally summarized as a practical guide.

Lingyun online examination system

Online examination system is a collection of modern test theory, methods and modern information technology mean intelligent online examination system, make full use of the school's existing computer software and hardware resources and network resources to achieve paperless examination to make up for the lack of traditional manual examination. I mainly develop the system of the background management subsystem, which includes the trial management, test questions, online volume, and test paper management, arrange exams, check the exam rankings, and check the test scores and other functions.This paper describes the development process of online examination system, using B-S mode, using SSH (Struts+Spring+Hibernate) framework, JavaScript, Servlet, Ajax and other major technologies, the use of Oracle database, MyEclipse development tools. This paper mainly introduces the development process of an online examination system, including requirements analysis, data flow analysis, functional design, database design, system implementation, system debugging and testing.