Impact evaluation

Summary

Impact evaluation assesses the changes that can be attributed to a particular intervention, such as a project, program or policy, both the intended ones, as well as ideally the unintended ones. In contrast to outcome monitoring, which examines whether targets have been achieved, impact evaluation is structured to answer the question: how would outcomes such as participants' well-being have changed if the intervention had not been undertaken? This involves counterfactual analysis, that is, "a comparison between what actually happened and what would have happened in the absence of the intervention." Impact evaluations seek to answer cause-and-effect questions. In other words, they look for the changes in outcome that are directly attributable to a program. Impact evaluation helps people answer key questions for evidence-based policy making: what works, what doesn't, where, why and for how much? It has received increasing attention in policy making in recent years in the context of both

Official source

https://en.wikipedia.org/wiki/Impact_evaluation

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Although disaster risk management is becoming increasingly important in development cooperation, there is still a lack of robust evidence regarding its effectiveness. Few studies based on a counterfactual have been conducted in the fields of disaster management and disaster risk reduction. This article describes a methodological approach to enabling more rigorous evidence-based decision-making in community-based disaster risk mitigation, and notably for assessing the degree to which it increases preparedness for the adverse effects of hazards in vulnerable communities. The effects of actions designed to reduce disaster-induced loss, damage, injuries, fatalities and resource degradation were evaluated at a Swiss and Honduran Red Cross’ intervention area in Honduras. Our results show that the intervention has had a significant positive effect in three important areas of resilience: knowledge and preparedness (e.g., existence of an early warning system); social cohesion (e.g., community institutions); and management of natural assets (e.g., firewood consumption). Our findings indicate that the intervention program has enhanced the capacities of treated communities to prepare for future hazards and to respond in case of an emergency at both household and community levels.

2020

Dynamic Resource Allocation for Database Servers Running on Virtual Storage

Jin Chen, Daniel Lupei, Adrian Daniel Popescu

We introduce a novel multi-resource allocator to dynamically allocate resources for database servers running on virtual storage. Multi-resource allocation involves proportioning the database and storage server caches, and the storage bandwidth between applications according to overall performance goals. The problem is challenging due to the interplay between different resources, e.g., changing any cache quota affects the access pattern at the cache/disk levels below it in the storage hierarchy. We use a combination of on-line modeling and sampling to arrive at near-optimal configurations within minutes. The key idea is to incorporate access tracking and known resource dependencies e.g., due to cache replacement policies, into our performance model. In our experimental evaluation, we use both microbenchmarks and the industry standard benchmarks TPC-W and TPC-C. We show that our multi-resource allocation approach improves application performance by up to factors of 2.9 and 2.4 compared to state-of-the-art single-resource controllers, and their ad-hoc combination, respectively.

2009

DBToaster: Higher-order Delta Processing for Dynamic, Frequently Fresh Views

Yanif Ahmad, Christoph Koch, Daniel Lupei, Milos Nikolic, Andres Nötzli, Amir Shaikhha

Applications ranging from algorithmic trading to scientific data analysis require realtime analytics based on views over databases receiving thousands of updates each second. Such views have to be kept fresh at millisecond latencies. At the same time, these views have to support classical SQL, rather than window semantics, to enable applications that combine current with aged or historical data. In this article, we present the DBToaster system, which keeps materialized views of standard SQL queries continuously fresh as data changes very rapidly. This is achieved by a combination of aggressive compilation techniques and DBToaster's original recursive finite differencing technique which materializes a query and a set of its higher-order deltas as views. These views support each other's incremental maintenance, leading to a reduced overall view maintenance cost. This article provides a first description of the complete system, and a thorough experimental evaluation of its performance. DBToaster supports tens of thousands of complete view refreshes a second for a wide range of queries.

2013