The Bucharest Early Intervention Project (BEIP) is the first ever randomized controlled trial of foster care as an alternative to institutional care for young children. It involved a collaboration between American investigators and Romanian health and child protection professionals. We present a brief description of the Romanian context and the project itself before discussing a number of ethical issues raised by the project. Organized around a discussion of exploitation, risk/benefit ratio, and cultural sensitivity, we evaluate a number of ethical issues involved in the BEIP using the Ethical Clinical Research Framework and the Fair Benefits Framework. Based on this review, we conclude that notwithstanding challenging ethical dilemmas, the benefits of the project outweighed its risks. Throughout the planning and implementation of the project, ethical issues were a central focus of discussion among the investigators and in the collaboration between Americans and Romanians. Thoughtful discussions from multiple perspectives are necessary to conduct research that is ethically sound and scientifically meaningful.

The volume of spatial data generated and consumed is rising exponentially and new applications are emerging as the costs of storage, processing power and network bandwidth continue to decline. Database support for spatial operations is fast becoming a necessity rather than a niche feature provided by a few products. However, the spatial functionality offered by current commercial and open-source relational databases differs significantly in terms of available features, true geodetic support, spatial functions and indexing. Benchmarks play a crucial role in evaluating the functionality and performance of a particular database, both for application users and developers, and for the database developers themselves. In contrast to transaction processing, however, there is no standard, widely used benchmark for spatial database operations.In this paper, we present a spatial database benchmark called Jackpine. Our benchmark is portable (it can support any database with a JDBC driver implementation) and includes both micro benchmarks and macro workload scenarios. The micro benchmark component tests basic spatial operations in isolation; it consists of queries based on the Dimensionally Extended 9-intersection model of topological relations and queries based on spatial analysis functions. Each macro workload includes a series of queries that are based on a common spatial data application. These macro scenarios include map search and browsing, geocoding, reverse geocoding, flood risk analysis, land information management and toxic spill analysis. We use Jackpine to evaluate the spatial features in 2 open source databases and 1 commercial offering.

Spatial data analysis applications are emerging from a wide range of domains such as building information management, environmental assessments and medical imaging. Time-consuming computational geometry algorithms make these applications slow, even for medium-sized datasets. At the same time, there is a rapid expansion in available processing cores, through multicore machines and Cloud computing. The confluence of these trends demands effective parallelization of spatial query processing. Unfortunately, traditional parallel spatial databases are ill-equipped to deal with the performance heterogeneity that is common in the Cloud.We introduce Niharika, a parallel spatial data analysis infrastructure that exploits all available cores in a heterogeneous cluster. Niharika first uses a declustering technique that creates balanced spatial partitions. Then, Niharika adapts to performance heterogeneity and processing skew in the spatial dataset using dynamic loadbalancing. We evaluate Niharika with three load-balancing algorithms and two different spatial datasets (both from TIGER) using Amazon EC2 instances. Niharika adapts to the performance heterogeneity in the EC2 nodes, thereby achieving excellent speedups (e.g., 63.6X using 64 cores on 16 4-core EC2 nodes, in the best case) and outperforming an approach that does not adapt.

In this paper, we study parallelization of multiplayer games using software Transactional Memory (STM) support. We show that the STM provides not only ease of programming, but also better performance than that achievable with stateof-the-art lock-based programming, for this realistic high impact application.For this purpose, we use a game benchmark, SynQuake, that extracts the main data structures and the essential features of the popular game Quake. SynQuake can be driven with a synthetic workload generator that flexibly emulates client game actions and various hot-spot scenarios in the game world.We implement, evaluate and compare the STM version of SynQuake with a state-of-the-art lock-based parallelization of Quake, which we ported to SynQuake. While in STM-SynQuake support for maintaining the consistency of each complex game action is automatic, conservative locking of surrounding objects within a bounding box, for the duration of the game action is inherently needed in lockbased SynQuake. This leads to higher scalability of STMSynQuake versus lock-based SynQuake, due to a higher degree of false sharing in the latter. Task assignment to threads has a second-order effect on the scalability of STMSynQuake, due to its impact on the application's true sharing patterns. We show that a dynamic locality-aware task assignment to threads provides the best trade-off between load balancing and conflict reduction.

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