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Learn how to migrate your SAP data to Azure simply and successfully. Key Features * Learn why Azure is suitable for business-critical systems * Understand how to migrate your SAP infrastructure to Azure * Use Lift & shift migration, Lift & migrate, Lift & migrate to HANA, or Lift & transform to S/4HANA Book Description Cloud technologies have now reached a level where even the most critical business systems can run on them. For most organizations SAP is the key business system. If SAP is unavailable for any reason then potentially your business stops. Because of this, it is understandable that you will be concerned whether such a critical system can run in the public cloud. However, the days when you truly ran your IT system on-premises have long since gone. Most organizations have been getting rid of their own data centers and increasingly moving to co-location facilities. In this context the public cloud is nothing more than an additional virtual data center connected to your existing network. There are typically two main reasons why you may consider migrating SAP to Azure: You need to replace the infrastructure that is currently running SAP, or you want to migrate SAP to a new database. Depending on your goal SAP offers different migration paths. You can decide either to migrate the current workload to Azure as-is, or to combine it with changing the database and execute both activities as a single step. SAP on Azure Implementation Guide covers the main migration options to lead you through migrating your SAP data to Azure simply and successfully. What you will learn * Successfully migrate your SAP infrastructure to Azure * Understand the security benefits of Azure * See how Azure can scale to meet the most demanding of business needs * Ensure your SAP infrastructure maintains high availability * Increase business agility through cloud capabilities * Leverage cloud-native capabilities to enhance SAP Who this book is for SAP on Azure Implementation Guide is designed to benefit existing SAP architects looking to migrate their SAP infrastructure to Azure. Whether you are an architect implementing the migration or an IT decision maker evaluating the benefits of migration, this book is for you.

Most genomic variants associated with phenotypic traits or disease do not fall within gene coding regions, but in regulatory regions, rendering their interpretation difficult. We collected public data on epigenetic marks and transcription factor binding in human cell types and used it to construct an intuitive summary of regulatory regions in the human genome. We verified it against independent assays for sensitivity. The Ensembl Regulatory Build will be progressively enriched when more data is made available. It is freely available on the Ensembl browser, from the Ensembl Regulation MySQL database server and in a dedicated track hub.

Using heterogeneous processing devices, like GPUs, to accelerate relational database operations is a well-known strategy. In this context, the group by operation is highly interesting for two reasons. Firstly, it incurs large processing costs. Secondly, its results (i.e., aggregates) are usually small, reducing data movement costs whose compensation is a major challenge for heterogeneous computing. Generally, for group by computation on GPUs, one relies either on sorting or hashing. Today, empirical results suggest that hash-based approaches are superior. However, by concept, hashing induces an unpredictable memory access pattern conflicting with the architecture of GPUs. This motivates studying why current sort-based approaches are generally inferior. Our results indicate that current sorting solutions cannot exploit the full parallel power of modern GPUs. Experimentally, we show that the issue arises from the need to synchronize parallel threads that access the shared memory location containing the aggregates via atomics. Our quantification of the optimal performance motivates us to investigate how to minimize the overhead of atomics. This results in different variants using atomics, where the best variants almost mitigate the atomics overhead entirely. The results of a large-scale evaluation reveal that our approach achieves a 3x speed-up over existing sort-based approaches and up to 2x speed-up over hash-based approaches.

Abstract Study Objectives To examine the effects of cognitive behavioral treatments for insomnia (CBT-I) and pain (CBT-P) in patients with comorbid fibromyalgia and insomnia. Methods One hundred thirteen patients (Mage = 53, SD = 10.9) were randomized to eight sessions of CBT-I (n = 39), CBT-P (n = 37), or a waitlist control (WLC, n = 37). Primary (self-reported sleep onset latency [SOL], wake after sleep onset [WASO], sleep efficiency [SE], sleep quality [SQ], and pain ratings) and secondary outcomes (dysfunctional beliefs and attitudes about sleep [DBAS]; actigraphy and polysomnography SOL, WASO, and SE; McGill Pain Questionnaire; Pain Disability Index; depression; and anxiety) were examined at posttreatment and 6 months. Results Mixed effects analyses revealed that both treatments improved self-reported WASO, SE, and SQ relative to control at posttreatment and follow-up, with generally larger effect sizes for CBT-I. DBAS improved in CBT-I only. Pain and mood improvements did not differ by group. Clinical significance analyses revealed the proportion of participants no longer reporting difficulties initiating and maintaining sleep was higher for CBT-I posttreatment and for both treatments at 6 months relative to control. Few participants achieved >50% pain reductions. Proportion achieving pain reductions of >30% (~1/3) was higher for both treatments posttreatment and for CBT-I at 6 months relative to control. Conclusions CBT-I and CBT-P improved self-reported insomnia symptoms. CBT-I prompted improvements of larger magnitude that were maintained. Neither treatment improved pain or mood. However, both prompted clinically meaningful, immediate pain reductions in one third of patients. Improvements persisted for CBT-I, suggesting that CBT-I may provide better long-term pain reduction than CBT-P. Research identifying which patients benefit and mechanisms driving intervention effects is needed. Clinical Trial Sleep and Pain Interventions in Fibromyalgia (SPIN), clinicaltrials.gov, NCT02001077.

GPU accelerated query execution is still ongoing research in the database community, as GPUs continue to be heterogeneous in their architectures varying their capabilities (e.g., their newest selling point: tensor cores). Hence, many researchers come up with optimal operator implementations for a specific device generation involving tedious operator tuning by hand. Alternatively, there is a growing availability of GPU libraries providing optimized operators for various applications. However, the question arises of how mature these libraries are and whether they are fit to replace handwritten operator implementations not only w.r.t. implementation effort and portability but also performance. In this paper, we investigate various general-purpose libraries that are both portable and easy to use for arbitrary GPUs to test their production readiness on the example of database operations. To this end, we develop a framework to show the support of GPU libraries for database operations that allows a user to plug-in new libraries and custom-written code. Our framework allows for easy pluggability of new libraries for query execution using a simple task model. Using this framework, we develop multiple libraries (ArrayFire, Thrust, and boost.compute) supporting many database operations. We use these libraries to experiment with different devices to see the impact of the underlying device. Based on our experiments, we see a significant diversity in terms of performance among libraries. Furthermore, one of the fundamental database primitives—hashing, and thus hash joins—is currently not supported, leaving important tuning potential unused.

This case study describes the consolidation and migration of the University of New Mexico’s University Libraries’ database A-Z lists. A subject librarian lead the nine-month project that included most subject librarians, the electronic resources team, the Director of Collections, and the web & discovery librarian. The project also provided the UL the opportunity to review all the resources in the lists, and update all descriptions, and create new workflows for adding and managing a single list.

In recent studies, machine learning techniques have been employed to support or enhance cost-based query optimizers in DBMS. Although these approaches have shown superiority in certain benchmarks, they also suffer from certain drawbacks. These include unstable performance, high training costs, and slow model updating, which can be attributed to the inherent challenges of predicting the cost or latency of execution plans using machine learning models. In this paper, we introduce a le arning-to- r ank query o ptimizer, called Lero, which builds on top of the native query optimizer and continuously learns to improve query optimization. The key observation is that the relative order or rank of plans, rather than the exact cost or latency, is sufficient for query optimization. Lero employs a pairwise approach to train a classifier to compare any two plans and tell which one is better. Such a binary classification task is much easier than the regression task to predict the cost or latency, in terms of model efficiency and effectiveness. Rather than building a learned optimizer from scratch, Lero is designed to leverage decades of wisdom of databases and improve the native optimizer. With its non-intrusive design, Lero can be implemented on top of any existing DBMS with minimum integration efforts. We implement Lero and demonstrate its outstanding performance using PostgreSQL and Spark SQL. In our experiments, Lero achieves near-optimal performance on several benchmarks. It reduces the execution time of the native PostgreSQL optimizer by up to 70 % and other learned query optimizers by up to 37 % on single-machine environments. On distributed environments, our Lero improves the running time of the native Spark SQL optimizer by up to 27 % . Meanwhile, Lero continuously learns and automatically adapts to query workloads and changes in data.