搜索结果：Managing

We consider the problem of managing a portfolio of moving-band statistical arbitrages (MBSAs), inspired by the Markowitz optimization framework. We show how to manage a dynamic basket of MBSAs, and illustrate the method on recent historical data, showing that it can perform very well in terms of risk-adjusted return, essentially uncorrelated with the market.

The challenge of managing unstructured data represents perhaps the largest data management opportunity for our community since managing relational data. And yet we are risking letting this opportunity go by, ceding the playing field to other players, ranging from communities such as AI, KDD, IR, Web, and Semantic Web, to industrial players such as Google, Yahoo, and Microsoft. In this essay we explore what we can do to improve upon this situation. Drawing on the lessons learned while managing relational data, we outline a structured approach to managing unstructured data. We conclude by discussing the potential implications of this approach to managing other kinds of non-relational data, and to the identify of our field.

Purpose: Managing IT with firm performance has always been a debatable topic in literature and practice. Prior studies examining the above relationship have reported mixed results and have yet ignored the eminent managing IT practices. The purpose of this paper is to empirically investigate the relevance of ValIT 2.0 practice in managing IT investment, and its mediating role in the firm performance context. Design,methodology,approach:This paper developed on two themes of literature. First managing IT as a firm's IT capability in order to generate value from IT investment. Second IT as a firm's resource under resource-based view offers firm's competence that deploys potentials in achieving firm performance. The structural equation modeling with PLS techniques used for analyzing data collected from 176 organization's IT, and business executives in China. Findings: The results of this study show empirical evidence that Val-IT's components (value governance, portfolio management, and investment management) are significantly linked to the management of IT, and it found to be a significant mediator between Val-IT components and firm performance. Research implications: This research cont

Portfolio management is an essential component of investment strategy that aims to maximize returns while minimizing risk. This paper explores several portfolio management strategies, including asset allocation, diversification, active management, and risk management, and their importance in optimizing portfolio performance. These strategies are examined individually and in combination to demonstrate how they can help investors maximize alpha and minimize beta. Asset allocation is the process of dividing a portfolio among different asset classes to achieve the desired level of risk and return. Diversification involves spreading investments across different securities and sectors to minimize the impact of individual security or sector-specific risks. Active management involves security selection and risk management techniques to generate excess returns while minimizing losses. Risk management strategies, such as stop-loss orders and options strategies, aim to minimize losses in adverse market conditions. The importance of combining these strategies for optimizing portfolio performance is emphasized in this paper. The proper implementation of these strategies can help investors achie

Byte-addressable, non-volatile memory (NVM) is emerging as a promising technology. To facilitate its wide adoption, employing NVM in managed runtimes like JVM has proven to be an effective approach (i.e., managed NVM). However, such an approach is runtime specific, which lacks a generic abstraction across different managed languages. Similar to the well-known filesystem primitives that allow diverse programs to access same files via the block I/O interface, managed NVM deserves the same system-wide property for persistent objects across managed runtimes with low overhead. In this paper, we present UniHeap, a new NVM framework for managing persistent objects. It proposes a unified persistent object model that supports various managed languages, and manages NVM within a shared heap that enables cross-language persistent object sharing. UniHeap reduces the object persistence overhead by managing the shared heap in a log-structured manner and coalescing object updates during the garbage collection. We implement UniHeap as a generic framework and extend it to different managed runtimes that include HotSpot JVM, cPython, and JavaScript engine SpiderMonkey. We evaluate UniHeap with a vari

Non-functional requirements (NFR), which include performance, availability, and maintainability, are vitally important to overall software quality. However, research has shown NFRs are, in practice, poorly defined and difficult to verify. Continuous software engineering practices, which extend agile practices, emphasize fast paced, automated, and rapid release of software that poses additional challenges to handling NFRs. In this multi-case study we empirically investigated how three organizations, for which NFRs are paramount to their business survival, manage NFRs in their continuous practices. We describe four practices these companies use to manage NFRs, such as offloading NFRs to cloud providers or the use of metrics and continuous monitoring, both of which enable almost real-time feedback on managing the NFRs. However, managing NFRs comes at a cost as we also identified a number of challenges these organizations face while managing NFRs in their continuous software engineering practices. For example, the organizations in our study were able to realize an NFR by strategically and heavily investing in configuration management and infrastructure as code, in order to offload the

Managing requirements on quality aspects is an important issue in the development of software systems. Difficulties arise from expressing them appropriately what in turn results from the difficulty of the concept of quality itself. Building and using quality models is an approach to handle the complexity of software quality. A novel kind of quality models uses the activities performed on and with the software as an explicit dimension. These quality models are a well-suited basis for managing quality requirements from elicitation over refinement to assurance. The paper proposes such an approach and shows its applicability in an automotive case study.

Public organizations need innovative approaches for managing common goods and to explain the dynamics linking the (re)generation of common goods and organizational performance. Although system dynamics is recognised as a useful approach for managing common goods, public organizations rarely adopt the system dynamics for this goal. The paper aims to review the literature on the system dynamics and its recent application, known as dynamic performance management, to highlight the state of the art and future opportunities on the management of common goods. The authors analyzed 144 documents using a systematic literature review. The results obtained outline a fair number of documents, countries and journals involving the study of system dynamics, but do not cover sufficient research on the linking between the (re)generation of common goods and organizational performance. This paper outlines academic and practical contributions. Firstly, it contributes to the theory of common goods. It provides insight for linking the management of common goods and organizational performance through the use of dynamic performance management approach. Furthermore, it shows scholars the main research oppor

How stable is the performance of your flash-based Solid State Drives (SSDs)? This question is central for database designers and administrators, cloud service providers, and SSD constructors. The answer depends on write-amplification, i.e., garbage collection overhead. More specifically, the answer depends on how write-amplification evolves in time. How then can one model and manage write-amplification, especially when application workloads change? This is the focus of this paper. Managing write-amplification boils down to managing the surplus physical space, called over-provisioned space. Modern SSDs essentially separate the physical space into several partitions, based on the update frequency of the pages they contain, and divide the over-provisioned space among the groups so as to minimize write-amplification. We introduce Wolf, a block manager that allocates over-provisioned space to SSD partitions using a near-optimal closed-form expression, based on the sizes and update frequencies of groups of pages. Our evaluation shows that Wolf is robust to workloads change, with an improvement factor of 2 with respect to the state-of-the-art. We also show that Wolf performs comparably an

Here we shall consider a very popular practical applied problem of managing mode switching (in this work we are considering managing billing plans). Out of the two parties (service provider and service consumer), participating in the processes modelled here, we shall consider only a consumer type of a problem. Herein we provide formal characterization of the problem as well as the elements necessary for its solution. We shall consider full predicted costs, originating when switching to a billing plan as a target index. The work contains an example that provides a detailed view of the application technology referring to the suggested problem solution algorithm. Using the example's data we have performed the analysis measuring the problem's sensitivity in relation to the growth of the traffic volume. Herein we provided a polynomial approximation of the target index value depending on the traffic volume.

The industrial machine learning pipeline requires iterating on model features, training and deploying models, and monitoring deployed models at scale. Feature stores were developed to manage and standardize the engineer's workflow in this end-to-end pipeline, focusing on traditional tabular feature data. In recent years, however, model development has shifted towards using self-supervised pretrained embeddings as model features. Managing these embeddings and the downstream systems that use them introduces new challenges with respect to managing embedding training data, measuring embedding quality, and monitoring downstream models that use embeddings. These challenges are largely unaddressed in standard feature stores. Our goal in this tutorial is to introduce the feature store system and discuss the challenges and current solutions to managing these new embedding-centric pipelines.

The memory controller is in charge of managing DRAM maintenance operations (e.g., refresh, RowHammer protection, memory scrubbing) to reliably operate modern DRAM chips. Implementing new maintenance operations often necessitates modifications in the DRAM interface, memory controller, and potentially other system components. Such modifications are only possible with a new DRAM standard, which takes a long time to develop, likely leading to slow progress in the adoption of new architectural techniques in DRAM chips. We propose a new low-cost DRAM architecture, Self-Managing DRAM (SMD), that enables autonomous in-DRAM maintenance operations by transferring the responsibility for controlling maintenance operations from the memory controller to the SMD chip. To enable autonomous maintenance operations, we make a single modification to the DRAM interface, such that an SMD chip rejects memory controller accesses to DRAM regions under maintenance, while allowing memory accesses to others. Thus, SMD enables 1) implementing new in-DRAM maintenance mechanisms (or modifying existing ones) with no further changes in the DRAM interface or other system components, and 2) overlapping the latency o

Interactions between cloud services result in service dependencies. Evaluating and managing the cascading impacts caused by service dependencies is critical to the reliability of cloud systems. This paper summarizes the dependency types in cloud systems and demonstrates the design of the Dependency Management System (DMS), a platform for managing the service dependencies in the production cloud system. DMS features full-lifecycle support for service reliability (i.e., initial service deployment, service upgrade, proactive architectural optimization, and reactive failure mitigation) and refined characterization of the intensity of dependencies.

Grid-based technologies are emerging as a potential open-source standards-based solution for managing and collabo-rating distributed resources. In view of these new computing solutions, the Mammogrid project is developing a service-based and Grid-aware application which manages a Euro-pean-wide database of mammograms. Medical conditions such as breast cancer, and mammograms as images, are ex-tremely complex with many dimensions of variability across the population. An effective solution for the management of disparate mammogram data sources is a federation of autonomous multi-centre sites which transcends national boundaries. The Mammogrid solution utilizes the Grid tech-nologies to integrate geographically distributed data sets. The Mammogrid application will explore the potential of the Grid to support effective co-working among radiologists through-out the EU. This paper outlines the Mammogrid service-based approach in managing a federation of grid-connected mam-mography databases.

In this paper we consider robust models for emergency staff deployment in the event of a flu pandemic. We focus on managing critical staff levels at organizations that must remain operational during such an event, and develop methodologies for managing emergency resources with the goal of minimizing the impact of the pandemic. We present numerical experiments using realistic data to study the effectiveness of our approach. The underlying methodology is that of robust optimization; we model the problem as an infinite linear program which is approximately solved using a variant of Benders decomposition.