TRANSFORMERS: Robust spatial joins on non-uniform data distributions

Spatial joins are becoming increasingly ubiquitous in many applications, particularly in the scientific domain. While several approaches have been proposed for joining spatial datasets, each of them has a strength for a particular type of density ratio among the joined datasets. More generally, no single proposed method can efficiently join two spatial datasets in a robust manner with respect to their data distributions. Some approaches do well for datasets with contrasting densities while others do better with similar densities. None of them does well when the datasets have locally divergent data distributions. In this paper we develop TRANSFORMERS, an efficient and robust spatial join approach that is indifferent to such variations of distribution among the joined data. TRANSFORMERS achieves this feat by departing from the state-of-the-art through adapting the join strategy and data layout to local density variations among the joined data. It employs a join method based on data-oriented partitioning when joining areas of substantially different local densities, whereas it uses big partitions (as in space-oriented partitioning) when the densities are similar, while seamlessly switching among these two strategies at runtime. We experimentally demonstrate that TRANSFORMERS outperforms state-of-the-art approaches by a factor of between 2 and 8

Microbial Translocation Is Associated with Increased Monocyte Activation and Dementia in AIDS Patients

Elevated plasma lipopolysaccharide (LPS), an indicator of microbial translocation from the gut, is a likely cause of systemic immune activation in chronic HIV infection. LPS induces monocyte activation and trafficking into brain, which are key mechanisms in the pathogenesis of HIV-associated dementia (HAD). To determine whether high LPS levels are associated with increased monocyte activation and HAD, we obtained peripheral blood samples from AIDS patients and examined plasma LPS by Limulus amebocyte lysate (LAL) assay, peripheral blood monocytes by FACS, and soluble markers of monocyte activation by ELISA. Purified monocytes were isolated by FACS sorting, and HIV DNA and RNA levels were quantified by real time PCR. Circulating monocytes expressed high levels of the activation markers CD69 and HLA-DR, and harbored low levels of HIV compared to CD4+ T-cells. High plasma LPS levels were associated with increased plasma sCD14 and LPS-binding protein (LBP) levels, and low endotoxin core antibody levels. LPS levels were higher in HAD patients compared to control groups, and were associated with HAD independently of plasma viral load and CD4 counts. LPS levels were higher in AIDS patients using intravenous heroin and/or ethanol, or with Hepatitis C virus (HCV) co-infection, compared to control groups. These results suggest a role for elevated LPS levels in driving monocyte activation in AIDS, thereby contributing to the pathogenesis of HAD, and provide evidence that cofactors linked to substance abuse and HCV co-infection influence these processes

BLOCK: Efficient Execution of Spatial Range Queries in Main-Memory.

The execution of spatial range queries is at the core of many applications, particularly in the simulation sciences but also in many other domains. Although main memory in desktop and supercomputers alike has grown considerably in recent years, most spatial indexes supporting the efficient execution of range queries are still only optimized for disk access (minimizing disk page reads). Recent research has primarily focused on the optimization of known disk-based approaches for memory (through cache alignment etc.) but has not fundamentally revisited index structures for memory. In this paper we develop BLOCK, a novel approach to execute range queries on spatial data featuring volumetric objects in main memory. Our approach is built on the key insight that in-memory approaches need to be optimized to reduce the number of intersection tests (between objects and query but also in the index structure). Our experimental results show that BLOCK outperforms known in-memory indexes as well as in-memory implementations of disk-based spatial indexes up to a factor of 7. The experiments show that it is more scalable than competing approaches as the data sets become denser

Conjunctival cytology in glaucomatous patients using long-term topical therapy

<b>Background:</b> Long-term use of antiglaucoma drugs induces adverse changes on the conjunctival surface. <b> Aim:</b> To evaluate the cytological changes in the conjunctival scrape smears of patients receiving long-term antiglaucoma medication and their histopathological correlation. <b> Materials and Methods:</b> Conjunctival scrape smears were taken from the eyes of patients on long-term antiglaucoma therapy for over three months (<i>n</i> = 75), patients taking antiglaucoma medication for less than three months (<i>n</i> = 100) and from glaucomatous patients in whom trabeculectomy was done as a primary procedure. Inflammatory cell counts, fibroblasts, and the degree of metaplasia were then evaluated both cytologically and histologically. The <i>t</i>-test was used to determine the predictive values of these parameters for the surgical outcome of trabeculectomies. <b> Results:</b> Long-term use of antiglaucoma therapy leads to a higher stage of metaplasia with an increase in the number of fibroblasts, subepithelial collagen deposition, and inflammatory infiltrate within the substantia propria of the conjunctiva. <b> Conclusions:</b> Long-term antiglaucoma medications induce a significant degree of metaplasia in the conjunctival surface that adversely affects the outcome of filtration surgery

TOUCH: In-memory spatial join by hierarchical data-oriented partitioning

10.1145/2463676.2463700Proceedings of the ACM SIGMOD International Conference on Management of Data701-71

THERMAL-JOIN: A Scalable Spatial Join for Dynamic Workloads

Simulations have become ubiquitous in many domains of science. Today scientists study natural phenomena by first building massive three-dimensional spatial models and then by simulating the models at discrete intervals of time to mimic the behavior of natural phenomena. One frequently occurring challenge during simulations is the repeated computation of spatial self-joins of the model at each simulation time step. The join is performed to access a group of neighboring spatial objects (groups of particles, molecules or cosmological objects) so that scientists can calculate the cumulative effect (like gravitational force) on an object. Computing a self-join even in memory, soon becomes a performance bottleneck in simulation applications. The problem becomes even worse as scientists continue to improve the precision of simulations by increasing the number as well as the size (3D extent) of the objects. This leads to an exponential increase in join selectivity that challenges the performance and scalability of state-of-the-art approaches. We propose THERMAL-JOIN, a novel spatial self-join algorithm for dynamic memory-resident workloads. The algorithm groups objects in spatial proximity together into hot spots. Hot spots minimize the cost of computing join as objects assigned to a hot spot are guaranteed to overlap with each other. Using a nested spatial grid, THERMAL-JOIN partitions and indexes the dataset to locate hot spots. With experiments we show that our approach provides a speedup between 8 to 12x compared to the state of the art and also scales as scientists improve the precision of their simulations