In-memory Technology Research Articles

Positive feedback field effect transistor based on vertical NAND flash structure for in-memory computing

The distance between memory and central processing unit has led to a memory wall. To solve it, an in-memory technology that performs both memory and computation has been studied. To realize an ideal in-memory computing, we propose a positive feedback FET based on vertical NAND flash structure that can act as a memory and perform computation. The device can reconfigure the processing operations into AND or OR operations depending on the control gate bias. It performs memory by accumulating charge in the body, and logic operations can be performed by reading data stored in the charge trap layer. After this, it can also perform a writing operation. This component enables memory and read-compute-write operations, making it capable of implementing intrinsic in-memory computing. As a result, in this study, we designed and verified a structure that implements the core principles of in-memory computing.

The Implications of Robotic Process Automation in the Automation of Economic Processes: A Case Study of Fruit Processing Factory

For several years, big companies in Romania and beyond have faced various challenges, including digitization. This involves transitioning to new versions of integrated ERP systems, incorporating cutting-edge database trends like in-memory technology and SAP HANA, and adopting Robotic Process Automation (RPA) for modelling the economic processes used, like order to cash or procure to pay. While the deadline for SAP solutions is set for 2027, the preparations for this shift are extensive and require a mixed team of engineers and economists. Consequently, many SAP clients are exploring using auxiliary interfaces and RPA to streamline economic processes. To provide insights into potential solutions, this article presents a study on the strategies adopted by one large company active in fruit processing, from production to sales and distribution. This study aims to show how intelligent technologies can help company departments automate economic processes. The study draws on questionnaires sent to the users from the mentioned company and the authors' own experience in creating interfaces used for economic processes. The research results confirm that it is time when certain stages of the economic process to be performed by a software bot, which will allow company employees to have the necessary time to study and improve work with economic processes. Companies should prioritize the intelligent processing, storage, and distribution of data to beneficiaries, especially regarding repetitive tasks. Every company should prioritize attracting European funds for automation and digitization, as incorporating intelligent technologies into daily work is essential.

Non-volatile memory based in-memory computing technology

By integrating the storage and computing functions on the fundamental elements, computing in-memory (CIM) technology is widely considered as a novel computational paradigm that can break the bottleneck of Von Neumann architecture. Nonvolatile memory device is an appropriate hardware implementation approach of CIM, which possess significantly advantages, such as excellent scalability, low consumption, and versatility. In this paper, first we introduce the basic concept of CIM, including the technical background and technical characteristics. Then, we review the traditional and novel nonvolatile memory devices, flash and resistive random access memory (RRAM), used in non-volatile based computing in-memory (nvCIM) system. After that, we explain the operation modes of nvCIM: in-memory analog computing and in-memory digital computing. In addition, the applications of nvCIM are also discussed, including deep learning accelerator, neuromorphic computing, and stateful logic. Finally, we summarize the current research advances in nvCIM and provide an outlook on possible research directions in the future.

Forensic Analysis of Braking Classification Based on Acceleration, Jerk, and Velocity Data

Nowadays, four-wheeled vehicles are equipped with an event data recorder (EDR) device to record sensors data. With advances in-memory technology, EDR provides evidence for forensic analysis after an accident happens, that uses information technology to facilitate forensic analysis to provide complete and valuable results using digital investigations. Several types of research have been conducted to reconstruct accidents from forensic data and Fuzzy Logic is an alternative method for classifying crash data taken from the accelerometer due to less complexity of implementation. Vehicle braking data is one of the most important evidence for digital investigation, since braking is a complex process determined by many factors, such as the condition of the vehicle, road construction, and the driver’s physiological condition. However, the existing digital investigation still process vehicle speed, deceleration, and varia- tion time of deceleration (known as a jerk) in separated manner to determine braking distance, driver response time, and braking category. The problem identified in this paper is how to use deceleration, velocity, and jerk to categorize the braking evidence forensic analysis. In this paper, forensic analysis is limited to produce forensic evident of braking events based on the collected data. The contribution of this paper is to propose a braking detection model by combining acceleration, speed, and jerk data into a Fuzzy Inference System. As a result, a forensic analysis of braking data can better understand the braking maneuvers, which can be further developed to identify the cause of the accident and provide recommendations on which actions to include in future analyses.

The Design of Power Security Defense System Based on Resource Pool Cloud Computing Technology

In order to realize the power system defense security, this article puts forward the idea and method of constructing power dispatching automation systems with a cloud computing architecture and realizes the unified management of distributed resources with server virtualization technology. Real-time online migration of each module of the scheduling system is realized by using the in-memory data transfer technology. The multi-node network heartbeat detection technology is used to realize the complete monitoring of the server cluster. In the form of an independent disk array, the fault node is removed, and the service is restored automatically. The whole disaster reserve of the system is realized by means of remote resource mapping. System analysis results show that compared with traditional architecture, the service interruption probability of the new scheduling automation system is effectively reduced. Fault redundancy capacity in the station is increased from a key module 2 node to multi-node protection of all modules.

In-memory big data analytics under space constraints using dynamic programming

The emergence of persistent memories has powered the data processing with the in-memory environment and in-memory data analytics have become an advance of high-performance data processing. Recent explorations of using in-memory technologies address the improvement of the memory performance from re-designing file systems. Most current approaches mitigate data exchanges between buffers and disks by migrating workload to memories. However, this type of solutions will be encountering the restriction of the memory size with the rapid growth of the application volume. This paper focuses on the issue caused by the large amount of data processing within in-memory systems and proposes a novel approach that is designed to dynamically determine whether the data processing should be accomplished in the memory. The proposed approach is called Smart In-Memory Data Analytics Manager (SIM-DAM) model, which utilizes a dynamic working manner of the file system, as well as fully uses hardware mappings. The experimental results obtained from our laboratory evaluations represent that the throughputs of SIM-DAM can achieve a high-level performance with different input data sizes without the constraints of the memories’ spaces.

A perspective on applications of in-memory and associative approaches supporting cultural big data analytics

Business intelligence, advanced analytics, big data, in-memory database and associative technologies are actually the key enablers for enhanced business decision making. In this paper, we provide a perspective on applications of in-memory approaches supporting analytics in the field of the cultural heritage applied to information resources including structured and unstructured contents, geo-spatial and social network data, multimedia (MM), multiple domain vocabularies, classifiers and ontologies. The proposed approach has been implemented in an information system exploiting associative in-memory technologies in a cloud context, as well as integrating semantic technologies for merging and analysing information coming from heterogeneous sources. We analyse and describe the application of this system to trace a behavioural and interest profile of users and visitors for cultural events (exhibitions, museums, etc.) and territorial (tourist areas and routes including cultural resources, historical down-town, and archaeological sites). The results of ongoing experimentation encourage a business intelligence approach which is suitable for supporting cultural heritage asset crowdsourcing, promotion, publication, management and usage.

Building novel capabilities to enable business intelligence agility: results from a quantitative study

The class of business intelligence (BI) systems is widely used to guide decisions in all kinds of organizations and across hierarchical levels and functions. Organizations have launched many initiatives to accomplish adequate and timely decision support as an important factor to achieve and sustain competitive advantage. Given today’s turbulent environments it is increasingly challenging to bridge the gap between establishing a long-term strategy and quickly adopting to the dynamics in market competition. BI must address this field of tension as it was originally used to retrospectively reflect an organization’s performance and build upon stability and efficiency. This study aims to understand and achieve an agile BI from a dynamic capability perspective. Therefore, we investigate how dynamic BI capabilities, i.e., adoption of assets, market understanding, and intimacy as well as business operations, impact the agility of BI. Starting from a literature review of dynamic capabilities in information systems and BI, we propose hypotheses to connect dynamic BI capabilities with the agility to provide information. The derived hypotheses were tested using partial least squares structural equation modeling on data collected in a questionnaire-based survey. The results show that the lens of dynamic capabilities provides useful means to foster BI agility. The study identifies that technological advancements like in-memory technology seem to be a technology enabler for BI agility. However, an adequate adoption and integration of BI assets as well as the focus on market orientation and business operations are crucial to reach BI agility.

How real-time analytics changes the face of the sports betting industry

Sports books are turning to technology to help with risk management, customer experience, social media interaction and spotting fraudulent activity. This paper looks at the differences between the latest data warehousing technology, delving into the pros and cons of data lakes, streaming and in-memory technology, as well as analytics. The paper provides a roadmap of how to utilise these technologies to increase customer acquisition, engagement, segmentation and player profiling. It also describes the creation of a risk management engine to help sports books hedge their positions. Finally, it discusses how social media marketing can be utilised not to only drive engagement but also increase a sports book's revenue.

An associative engines based approach supporting collaborative analytics in the Internet of cultural things

In this paper we illustrate an integrated approach combining Business Intelligence, Big Data and Internet of Things (IoT), which is applied to information resources including structured and unstructured contents, Geo-Spatial and Social Network data, Multimedia (MM), multiple domain vocabularies, classifiers and ontologies. This is implemented in an information system which exploits Associative in-memory technologies in the context of Cloud Computing, as well as Semantic technologies for merging and analyzing information coming from heterogeneous sources. The primary aim is supporting Cultural Heritage Asset crowdsourcing, promotion, publication, management and usage. We describe and discuss, in particular, the application of this system for the analysis of behavior and interest of visitors in different types of populations and visits: on-site/ad-hoc (exhibitions, museums, cultural events) and territorial (historical downtown, archaeological or other touristic areas and routes including cultural resources). In this way it will be possible to provide a common ICT infrastructure and a set of advanced services for all types of subjects interested in the Cultural Heritage domain. The results of the experimentation encourage a Business Intelligence approach which is suitable for both nonprofit, research and business oriented organizations.

Data Architecture For A Clincal Data Repository - Evaluation And Design At Charité Universitätsmedizin Berlin

Introduction/ Background The translation of scientific results into new and more effective diagnostic and therapeutic procedures is a milestone in the advancement of medicine. German clinics collect large amounts of data on every patient, which is used to: evaluate treatment; justify expense reports; operate quality assurance and to keep aftercare Physicians/caregiver informed. However, so far there are only fragmentary approaches to using this data resource. The combination of phenotype information from clinical routines with information about samples held in the biobank systems and linked with genetic information must be achievable. Therefore a data architecture must be designed which allows an access of all relevant patient data stored in different source systems under the aspects of data security, data protection, data integrity and semantic interoperability. The following paper focused on the integration of patient clinical data and patient sample data stored in the biobank system. The central role plays the design and implementation of a Clinical Data Repository (CDR) at the Charite. Aims Definition of the essential requirements of a Clinical Data Repository regarding data security, data protection, data integrity and semantic interoperability. Definition of standardized data flow from clinical patient system into the clinical data repository. Definition of the data model of the clinical data repository. Methods In order to design the central Clinical Data repository an investigation of existing clinical and research system landscape at the Charite took place. The existing solutions were evaluated regarding usage, level of penetration, standards in interoperability and supported interfaces. The analysis of Clinical Data Repository requirements was based on the methodology of Requirements-Engineering. This methodology is very often used for development of complex IT systems in order to gain a common understanding on user side and developer side too. In 2013 and 2014 Charite supported a project to identify the main system demands on a clinical scientist workplace. The essential demands incorporated the Clinical Data Repository requirement analysis. From the technically point of view the Clinical Data Repository has to deal with a large amount of data in terms of storage, scalability, stability, fast accessibility and search and the support of data analytics. The introduction of In-Memory technology has enabled a paradigm shift in analytic applications, with many new possibilities. It is now possible to have all working data in the main memory, which means that internal database programming can be implemented to execute computer and data intensive algorithms without having to access data over slow interfaces. The Clinical Data Repository will be based on latest In-Memory technologies to allow researchers and physicians real time access to huge amounts of data. Results In the first phase the design of data architecture and a core set of clinical date is defined. A pilot implementation of a Clinical Data Repository will provide a research space where clinical data and research data is accessible for researchers and physicians. To correspond with data protection laws this data will be anonymized, pseudonymized and stored securely. The central biobank system is connected via an identify management system with the Clinical Data Repository. The CDR allows requests to identify groups of patient with include or exclude criteria from clinical workplace as well from biobank system.

A Service Oriented Framework for Analysing Social Network Activities

Analysing and monitoring Social Networking activities raise multiple challenges for the evolution of Service Oriented Systems Engineering. This is particularly evident for event detection in social networks and, more in general, for large-scale Social Analytics, which require continuous processing of data. In this paper we present a service oriented framework exploring effective ways to leverage the opportunities coming from innovations and evolutions in computational power, storage, and infrastructures, with particular focus on modern architectures including in-memory database technology, in-database computation, massive parallel processing, Open Data Services, and scalability with multi-node clusters in Cloud. A prototype of this system was experimented in the contest of a specific kind of social event, an art exhibition of sculptures, where the system collected and analyzed in real-time the tweets issued in an entire region, including exhibition sites, and continuously updated analytical dashboards placed in one of the exhibition rooms.

In-memory data grid 기술을 활용한 택시 애플리케이션 성능 향상 기법 연구

In-memory data grid 기술을 활용한 택시 애플리케이션 성능 향상 기법 연구

Application of inmemory data management technology in genealogy information system

Application of inmemory data management technology in genealogy information system

Business value of in-memory technology – multiple-case study insights

Purpose – The purpose of this paper is to assess the business value of in-memory computing (IMC) technology by analyzing its organizational impact in different application scenarios. Design/methodology/approach – This research applies a multiple-case study methodology analyzing five cases of IMC application scenarios in five large European industrial and service-sector companies. Findings – Results show that IMC can deliver business value in various applications ranging from advanced analytic insights to support of real-time processes. This enables higher-level organizational advantages like data-driven decision making, superior transparency of operations, and experience with Big Data technology. The findings are summarized in a business value generation model which captures the business benefits along with preceding enabling changes in the organizational environment. Practical implications – Results aid managers in identifying different application scenarios where IMC technology may generate value for their organizations from business and IT management perspectives. The research also sheds light on the socio-technical factors that influence the likelihood of success or failure of IMC initiatives. Originality/value – This research is among the first to model the business value creation process of in-memory technology based on insights from multiple implemented applications in different industries.

Toward Agile BI By Using In-Memory Analytics

This paper explores one of the newer technologies related to the field of Business Intelligence: in-memory technology. The new class of in-memory BI tools turns a BI solution into an agile BI solution. Also, the paper focuses on the main data models used by in-memory BI technologies and tries to answer following questions: Which are the main characteristics of an agile data model? And, which is the best data model that can be used for enabling an agile BI solution?Keywords: In-Memory Analytics, Associative Data Model, Interactive Visualization, Asso- ciative Search, In-Memory Technology(ProQuest: ... denotes formulae omitted.)1 IntroductionI n the last years, emerging technologies such as interactive visualization, in-memory analytics and associative search marginalized IT role in building BI solutions. Figure 1 shows the trend in use of these technologies (as search terms) using Google Trends. We see that the interest for these technologies has increased in the last years.Also, Figure 2 shows how these technologies affect businesses. These technologies allow business people to do basic exploration of larger data sets and to find better answers to business problems. In-memory technology has the potential to help BI systems to be- come more agile, more flexible and more re- sponsive to changing business requirements. This section takes a look at the pros and cons of in-memory BI. The primary goal of the in- memory BI technology is to replace tradi- tional disk-based BI solutions. The important differences between them are: speed, volume, persistence and price [1].For decades BI solutions have been plagued by slow response times, but speed is very important in analysis and in-memory BI technologies are faster than disk-based BI technologies. In-memory BI technologies load the entire dataset into RAM before a query can be executed by users. Also, most of them can save significant development time by eliminating the need for aggregates and designing of cubes and star schemas.The speed of in-memory technology makes possible more analytics iterations within a given time. Ken Campbell, director of PwC Consulting Services company notes: Hav- ing a big data set in one location gives you more flexibility. T-Mobile, one of SAP's cus- tomers for HANA, claims that reports that previously took hours to generate now take seconds. HANA did require extensive tuning for this purpose.[2].But RAM is expensive compared to disk. memory technologies use compression tech- niques to represent more data in RAM. Also, most of in-memory technologies use colum- nar compression to improve compression ef- ficiency.The traditional disk-based BI solutions use query-based architectures such as: ROLAP, MOLAP and HOLAP. ROLAP uses SQL or another query language to extract detail data, to calculate aggregates and store them in ag- gregate tables. Detail data are stored in data warehouses or data marts (disk-based persis- tence) and are used when necessary. MOLAP pre-aggregates data using MDX or another multidimensional query language. HOLAP (hybrid OLAP) is a combination of the two above architectures. But these query-based solutions don't maintain the relationships among queries. Some of in-memory BI tech- nologies can maintain the relationships among queries.Today, one of challenges of BI is to allow users to become less dependent on IT. BI so- lutions must be easier to be used by all BI users. Traditional BI solutions don't provide a dynamic data exploration and interactive visualization. The in-memory BI tools like Qlikview, Tableau, Tibco Spotfire can sim- plify a larger number of tasks in an analytics workflow. The director of Visual Analysis at Tableau Software, Jock Mackinlay says In- side Tableau, we use Tableau everywhere, from the receptionist who's keeping track of conference room utilization to the salespeo- ple who are monitoring their pipelines [2]. Tableau Software, a leader in Magic Quad- rant for Business Intelligence and analytics platforms/Garter (2014) is an example of how these BI tools change the businesses. …

How In-memory Technology Can Create Business Value: Insights from the Hilti Case

With in-memory technology, all data and applications are kept in the computer’s main memory to avoid expensive mechanical hard-drive I/O access, reduce latency times, and increase the ability to process large volumes of data or complex data. In this “innovation and novel concepts” article, we discuss how in-memory technology may create business value. Based on our experiences in collaborating with the Hilti Corporation, one of the first adopters of SAP’s in-memory technology appliance (SAP HANA), we describe and discuss illustrative application scenarios that are made possible through the increased computing power offered by in-memory technology. Based on these scenarios, we identify principles of value creation through in-memory technology: the first-order effects of reduced latency times and increased ability to process large volumes of complex data (big data processing) that lead to the second-order effects of advanced business analytics and the convergence of OLTP and OLAP that themselves lead to business value through improved organizational performance.

Designing Real Time Business Intelligence Reporting for Better Business Insights

warehousing has been one of the key implementation practices which was followed in many industries. With constant innovation in hardware and enterprise software's the decision support systems were able to provide more accurate information about the business based on the available historical data. With the rapid change in customer buying habits and frequently changing markets every business needs more insight into all the available data that is in the database. A successful organization needs to take better business decision based on the most recent data. Hence if an analytical reporting tool can provide the business insights based on the real time data available it will definitely help the organizations to understand the business in real time and take better decision on the fly.. The usage of in-memory computing and data storage options like columnar database capability for business intelligence applications can be highly considered for designing the business intelligence systems that provide real time business intelligence reports. This article will analyze the effectiveness of having a real time analytical reporting tool which also use the in-memory technology for business intelligence based applications and see how it can help in making organization to take better business decisions. Keywordstime business intelligence reporting, Performance dashboards, In-Memory analytics.

Next Generation Data Warehouse and In-Memory Analytics

Business intelligence is becoming one of the key implementation practices in many industries, with the rapid change in customer buying habits and frequently changing markets every business needs more insight into all the available data that is in the database. Successful business needs to take analytical decision from all possible data that is available. Traditionally the business warehouse system used to get historical data from multiple sources and assist in the decision making process which actually takes time. But to adapt to the rapid changing market there is a need to analyze the business as it happens and take business decisions on real time. With the huge advancement in the hardware and data storage technologies and availability of 64 bit processors, it can help the business intelligence applications to fully utilize the potential of the latest hardware technologies available. The usage of in-memory computing and data storage options like columnar database capability for business intelligence applications can be highly considered for designing the next generation data warehouse systems. This article will analyze the effectiveness of using the in-memory technology for business intelligence based applications and see how it can help in increasing the performance of the business intelligence applications.

Comments on “In-Memory Databases in Business Information Systems”

In-memory databases, which keep the full data set in the main memory for reading accesses, and their application in business information systems, receive more and more attention due to the development and marketing of SAP technology HANA. In issue 6/2011 of the journal Business & Information Systems Engineering, a total of 11 authors discuss potential applications of in-memory databases in business scenarios in 6 articles (Loos et al. 2011). Thereby, questions about the capability of in-memory technology, resulting consequences for OLTP and OLAP applications as well as implications on the architecture of business information systems, amongst others, were in the focus of the discussion. After publication, the first author received feedback and suggestions. Three of these responses resulted in written statements: Strohmeier focuses on the question whether or not future OLTP and OLAP applications should be integrated. He states that concepts for both an operative decision support and for the provision of operative real-time information need to be developed. As a result, he sees a demand for more research in the area “Operational Business Intelligence”. Piller provides more examples of application scenarios, which emerge with in-memory databases. For instance, operative reporting, adaptive planning, explorative mass data analysis or the analysis of consumer data from embedded systems. Schutte explains, after a description of implicit presumptions and requirements for in-memory technology, the potential of in-memory technology using a practical example of the EDEKA group, which he sees in batch processes, initial supply, and release change. If you like to comment on this topic or another article of the journal Business & Information Systems Engineering, please send your feedback to loos@iwi.uni-sb.de.