Search

The integration of ArcGIS Enterprise with SAP HANA Cloud allows organizations to leverage the power of location intelligence for their business operations. With a central repository for all their data, organizations can gain a complete overview of their business and make better-informed decisions. The seamless integration of operational and locational data provides a high-performance environment that can scale to meet the needs of the largest enterprises. The use of dashboards, mobile applications, and reports integrated with dynamic maps can provide greater insight and understanding for organizations. Additionally, Mobolutions has developed a custom FIORI application, PM360, that integrates with ESRI to provide efficient asset management capabilities. The application is deployable on-premises or on the cloud, providing flexibility for customers.

The retail industry is quite exposed to fraudulent situations. Daily, thousands of transactions are processed, which may include some frauds difficult to detect, mainly when the perpetrators are the own employees at the retail stores. Large retailers with several stores across different locations may have considerable difficulty in detecting frauds involving their cashiers since they have to take into account different contexts of operation. To reduce fraud losses, retailers get an overview of the transactions in each store to filter the ones that look suspicious deviating from what would be normal. Data mining algorithms can be useful to detect anomalies, differentiating the normal from the abnormal. This study adopted the k-Means clustering algorithm for anomaly detection on a sample of 90 stores in a large food retail chain, revealing the existence of some outliers in the data. The anomaly detection process was fully implemented in SAP HANA XS Advanced using the Predictive Analysis Library (PAL). In the end, it was possible to identify the stores with abnormal behavior and conclude for the usefulness and ease of use of such a library, despite some lack of documentation to use it.

Existing binary join based plans may be suboptimal for important, emerging applications. Typical query optimizers enumerate plans using binary joins only. In this paper, we introduce the multi-way join aware optimizer in SAP HANA. The naive way to extend the existing query optimizer to be aware of multi-way joins ( m -way joins for short) is to enumerate m -way joins on top of a traditional binary join enumeration framework. However, many different binary joins correspond to the same m -way join. Thus, unnecessary join enumerations would be required for such naive integration. To solve this problem, we introduce the new concept of an m -way join unit and explain how the construction of join units is plugged into the SAP HANA query optimizer. We also provide a series of optimizer enhancements by exploiting m -way join unit characteristics. Using TPC-H and our customer workloads, we showcase the superiority of our m -way join aware optimizer.

Database replication is widely known and used for high availability or load balancing in many practical database systems. In this paper, we show how a replication engine can be used for three important practical cases that have not previously been studied very well. The three practical use cases include: 1) scaling out OLTP/OLAP-mixed workloads with partitioned replicas, 2) efficiently maintaining a distributed secondary index for a partitioned table, and 3) efficiently implementing an online re-partitioning operation. All three use cases are crucial for enabling a high-performance shared-nothing distributed database system. To support the three use cases more efficiently, we propose the concept of asymmetric-partition replication , so that replicas of a table can be independently partitioned regardless of whether or how its primary copy is partitioned. In addition, we propose the optimistic synchronous commit protocol which avoids the expensive two-phase commit without sacrificing transactional consistency. The proposed asymmetric-partition replication and its optimized commit protocol are incorporated in the production versions of the SAP HANA in-memory database system. Through extensive experiments, we demonstrate the significant benefits that the proposed replication engine brings to the three use cases.

Объектом исследования является крупная компания нефтегазового сектора. Целью выпускной квалификационной работы является описание разработки проекта и процесса внедрения модуля SAP BW, анализ и оценка рисков проекта. Задачи, которые решались в Ñ Ð¾Ð´Ðµ работы: Исследование объекта автоматизации и формирование требований к решению; Изучить SAP BW, СУБД SAP HANA и проанализировать ее преимущества перед аналогичными базами Ð´Ð°Ð½Ð½Ñ‹Ñ , на ÐºÐ¾Ñ‚Ð¾Ñ€Ñ‹Ñ Ð¼Ð¾Ð¶Ð½Ð¾ реализовывать проект SAP BW; Описание разработок; Изучение и описание этапов проекта и Ð¾ÑÐ½Ð¾Ð²Ð½Ñ‹Ñ Ñ€Ð°Ð±Ð¾Ñ‚; Изучение и описание рисков при внедрении данного проекта. Использованные методы: сбор, обработка и анализ Ð´Ð°Ð½Ð½Ñ‹Ñ , сравнительный анализ, моделирование бизнес-процессов, методы оценки эффективности проектов. Результат работы: Произведен анализ деятельности компании; Выявлен ряд проблем при формирования долгосрочной модели; Была выбрана Ð¿Ð¾Ð´Ñ Ð¾Ð´ÑÑ‰Ð°Ñ БД для разработки. Практическая значимость работы заключается в совершенствовании процесса долгосрочного планирования. Сотрудники получили быстрый доступ к проектной информации, повысилась эффективность использования Ñ‚Ñ€ÑƒÐ´Ð¾Ð²Ñ‹Ñ Ñ€ÐµÑÑƒÑ€ÑÐ¾Ð². Изменения положительно повлияли на финансовые показатели компании., The object of the study is a large company in the oil and gas sector.The purpose of the final qualification work is to describe the development of the project and the process of implementing the SAP BW module, analysis and assessment of project risks.Tasks that were solved during the work: The study of the automation object and the formation of requirements for the solution; Study SAP BW, SAP HANA DBMS and analyze its advantages over similar databases on which you can implement an SAP BW project; Description of developments; Study and description of the project stages and main works; Study and description of risks in the implementation of this project. Methods used: data collection, processing and analysis, comparative analysis, business process modeling, methods for evaluating the effectiveness of projects. The result of the work: The analysis of the company's activities was carried out; A number of problems have been identified in the formation of a long-term model; A suitable database has been selected for development.The practical significance of the work is to improve the process of long-term planning. Employees got quick access to project information, and the efficiency of using labor resources increased. The changes had a positive impact on the company's financial performance.

After a developer submits code, corresponding test cases arise to ensure the quality of software delivery. Test failures would occur during this period, such as crash, error, and timeout. Since it takes time for developers to resolve them, many duplicate failures will happen during this period. In the delivery practice of SAP HANA, crash triage is considered as the most time-consuming task. If duplicate crash failures can be automatically identified, the degree of automation will be significantly enhanced. To find such duplicates, we propose a training-based mathematical model that utilizes component information of SAP HANA to achieve better crash similarity comparison. We implement our approach in a tool named Knowledge-based Detector (K-Detector), which is verified by 11,208 samples and performs 0.986 in AUC. Furthermore, we have deployed K-Detector to the production environment, and it can save 97% human efforts in crash triage as statistics., Comment: 6 pages, 7 figures, ISSRE 2020

Wilkinson's Tests are used to benchmark the accuracy of some statistical functions in six SQL packages: Apache Hive, Microsoft Access, Microsoft SQL Server, MySQL, Oracle 11g SQL, and SAP Hana. Using the best choice of data type, we find that different packages use different rounding schemes, two packages use unreliable algorithms to compute the sample variance, one package returns the population standard deviation when the sample standard deviation is called, and one package has an unstable algorithm for computing the correlation coefficient. Using the wrong data type all but guarantees inaccurate results.

We present an overview of SAP HANA's Native Store Extension (NSE). This extension substantially increases database capacity, allowing to scale far beyond available system memory. NSE is based on a hybrid in-memory and paged column store architecture composed from data access primitives. These primitives enable the processing of hybrid columns using the same algorithms optimized for traditional HANA's in-memory columns. Using only three key primitives, we fabricated byte-compatible counterparts for complex memory resident data structures (e.g. dictionary and hash-index), compressed schemes (e.g. sparse and run-length encoding), and exotic data types (e.g. geo-spatial). We developed a new buffer cache which optimizes the management of paged resources by smart strategies sensitive to page type and access patterns. The buffer cache integrates with HANA's new execution engine that issues pipelined prefetch requests to improve disk access patterns. A novel load unit configuration, along with a unified persistence format, allows the hybrid column store to dynamically switch between in-memory and paged data access to balance performance and storage economy according to application demands while reducing Total Cost of Ownership (TCO). A new partitioning scheme supports load unit specification at table, partition, and column level. Finally, a new advisor recommends optimal load unit configurations. Our experiments illustrate the performance and memory footprint improvements on typical customer scenarios.

Over the last 20 years, the progress of information technology has allowed many companies to generate, integrate, store, and analyze data of unprecedented size and complexity. In many cases, this data is personal data and how it can be used is therefore subject to laws that depend on the specific countries and application domains. For example, the General Data Protection Regulation (GDPR) introduced in the European Union imposes strict rules on how personal data can be processed. Analyzing personal data can create tremendous value, but at the same time companies must ensure that they remain legally compliant. Unfortunately, existing systems offer only limited or no support at all for processing personal data in a privacy-aware manner. Approaches that have emerged from the academic and industrial research environments need to be integrated into large systems (like enterprise systems) in a manageable and scalable way. In many IT environments, it is also desirable and necessary to combine and to integrate personal data with other (non-personal) data in a seamless fashion. In this paper, we present the first steps that SAP has taken to provide its database management system SAP HANA with privacy-enhanced processing capabilities, referred to in the following as SAP HANA Data Anonymization. Various goals on both the conceptual and technical levels were followed with the aim of providing SAP customers today with an integrated processing environment for personal and non-personal data.

Treball Final de Grau en Enginyeria Informàtica. Codi: EI1054. Curs acadèmic: 2020/2021 Este proyecto consiste en desarrollar una herramienta web para la gestión interna de proyectos para la empresa Artero Consultores. El objetivo principal del proyecto es facilitar la planificación de los Proyectos de Implantación de SAP Business One mediante una herramienta software de gestión y mantenimiento de proyectos. Mediante este proyecto se pretende agilizar los trámites a la hora de crear y gestionar un nuevo proyecto. Mediante la creación de una página web que se encargue de planificar y gestionar los proyectos de implantación, es mucho más fácil poder prevenir cualquier tipo de error y, además, al estar todo en un mismo lugar, agiliza la obtención de información que normalmente estaría en otras áreas. This Project consists of developing a web tool for internal project management for the company Artero Consultores. The main objective of the project is to facilitate the planning of SAP Business One Implementation Projects through a software tool for the project management and maintenance. This project aims to streamline the procedures when creating and managing a new project. By creating a web that is responsible of planning and managing the implantation projects, it is much easier to prevent any kind of error and, in addition, since everything is in one place, it speeds up the obtaining of information that would normally be in other areas.

In the last years, performance modeling approaches have been proposed to tackle new concepts for modern In-Memory Database Systems (IMDB). While these approaches model specific performance-relevant aspects, workload representation during performance modeling is considered only marginally. Furthermore, the manual integration of workload into modeling approaches comes along with high effort and requires deep domain-specific knowledge. This paper presents our experience in representing workload within performance models for IMDB. In particular, we use a Markov chain-based approach to extract and reflect probabilistic user behavior during performance modeling. An automatic model generation process is integrated to simplify and reduce the effort for transferring workload characteristics from traces to performance models. In an experimental series running analytical and transactional workloads on an IMDB, we compare this approach with two other methods which rely on less granular data to reflect database workload within performance models, namely reproducing the relative invocation frequency of queries and using the same query execution probability. The results reveal a trade-off between accuracy and speed when simulating database workload. Markov chains are the most accurate independent from workload characteristics, but the relative invocation frequency approach is appropriate for scenarios where simulation speed is important.

This chapter gives an overview of the main modeling objects that are available in a SAP BW/4HANA application. I will walk you through the modeling objects from both the SAP HANA and SAP BW/4HANA worlds. In this chapter, you will learn how the existing modeling objects have changed and what the new modeling artifacts are.

Adaptive Case Management (ACM) can be a viable option for supporting knowledge-intensive processes like the incident management process (IM) in IT service management. To this end, a concept for an Adaptive Case Management system (ACMS) able to support the incident management process according to ITIL was described by Meyer, Wittmann, et al. (2021). To prove the concept’s feasibility, a prototype based on SAP HANA and the SAP Solution Manager, which uses ticket text similarity determination to recommend existing tickets to incident processors and tracks incident processor activities in SAP ABAP-based systems through SAP UI Logging is implemented in the following. The prototype is then shown to five industry-pundits in scope of a case study to evaluate the built artifact. Furthermore, an evaluatory analysis from a descriptive and an architectural point of view is done. The evaluations’ results show, that while not constituting a complete, high-fidelity solution, the prototype displays great promise in increasing efficiency and effectiveness in the IM process.

Now that we have discussed the possibility of exposing HANA objects (tables and views) in Chapter 3, this chapter explains how to implement a BW/4HANA data model using a field-based approach. We already know that by utilizing Open ODS Views objects, we can bring external data located in the SAP HANA database schema to the BW/4HANA layer. In this chapter, we will take advantage of Open ODS Views to build a virtual star schema, field-based data model in BW. I will also explain how you can automatically generate an intermediate data storage layer between the Open ODS View and source objects, without needing to edit the reporting layer.