XML Reconstruction View Selection in XML Databases: Complexity Analysis and Approximation Scheme

Query evaluation in an XML database requires reconstructing XML subtrees rooted at nodes found by an XML query. Since XML subtree reconstruction can be expensive, one approach to improve query response time is to use reconstruction views - materialized XML subtrees of an XML document, whose nodes are frequently accessed by XML queries. For this approach to be efficient, the principal requirement is a framework for view selection. In this work, we are the first to formalize and study the problem of XML reconstruction view selection. The input is a tree $T$, in which every node $i$ has a size $c_i$ and profit $p_i$, and the size limitation $C$. The target is to find a subset of subtrees rooted at nodes $i_1,\cdots, i_k$ respectively such that $c_{i_1}+\cdots +c_{i_k}\le C$, and $p_{i_1}+\cdots +p_{i_k}$ is maximal. Furthermore, there is no overlap between any two subtrees selected in the solution. We prove that this problem is NP-hard and present a fully polynomial-time approximation scheme (FPTAS) as a solution

CCL2 modulates cytokine production in cultured mouse astrocytes

<p>Abstract</p> <p>Background</p> <p>The chemokine CCL2 (also known as monocyte chemoattractant protein-1, or MCP-1) is upregulated in patients and rodent models of traumatic brain injury (TBI), contributing to post-traumatic neuroinflammation and degeneration by directing the infiltration of blood-derived macrophages into the injured brain. Our laboratory has previously reported that <it>Ccl2</it>-/- mice show reduced macrophage accumulation and tissue damage, corresponding to improved motor recovery, following experimental TBI. Surprisingly, <it>Ccl2</it>-deficient mice also exhibited delayed but exacerbated secretion of key proinflammatory cytokines in the injured cortex. Thus we sought to further characterise CCL2's potential ability to modulate immunoactivation of astrocytes <it>in vitro</it>.</p> <p>Methods</p> <p>Primary astrocytes were isolated from neonatal wild-type and <it>Ccl2</it>-deficient mice. Established astrocyte cultures were stimulated with various concentrations of lipopolysaccharide (LPS) and interleukin (IL)-1β for up to 24 hours. Separate experiments involved pre-incubation with mouse recombinant (r)CCL2 prior to IL-1β stimulation in wild-type cells. Following stimulation, cytokine secretion was measured in culture supernatant by immunoassays, whilst cytokine gene expression was quantified by real-time reverse transcriptase polymerase chain reaction.</p> <p>Results</p> <p>LPS (0.1-100 μg/ml; 8 h) induced the significantly greater secretion of five key cytokines and chemokines in <it>Ccl2</it>-/- astrocytes compared to wild-type cells. Consistently, IL-6 mRNA levels were 2-fold higher in <it>Ccl2</it>-deficient cells. IL-1β (10 and 50 ng/ml; 2-24 h) also resulted in exacerbated IL-6 production from <it>Ccl2</it>-/- cultures. Despite this, treatment of wild-type cultures with rCCL2 alone (50-500 ng/ml) did not induce cytokine/chemokine production by astrocytes. However, pre-incubation of wild-type astrocytes with rCCL2 (250 ng/ml, 12 h) prior to stimulation with IL-1β (10 ng/ml, 8 h) significantly reduced IL-6 protein and gene expression.</p> <p>Conclusions</p> <p>Our data indicate that astrocytes are likely responsible for the exacerbated cytokine response seen <it>in vivo </it>post-injury in the absence of CCL2. Furthermore, evidence that CCL2 inhibits cytokine production by astrocytes following IL-1β stimulation, suggests a novel, immunomodulatory role for this chemokine in acute neuroinflammation. Further investigation is required to determine the physiological relevance of this phenomenon, which may have implications for therapeutics targeting CCL2-mediated leukocyte infiltration following TBI.</p

Knowledge Rich Natural Language Queries over Structured Biological Databases

Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural language query expressed over a structured database schema to a structured query language is still a difficult task, and research to tame the complexity is intense. In this paper, we propose a multi-level knowledge-based middleware to facilitate such mappings that separate the conceptual level from the physical level. We augment these multi-level abstractions with a concept reasoner and a query strategy engine to dynamically link arbitrary natural language querying to well defined structured queries. We demonstrate the feasibility of our approach by presenting a Datalog based prototype system, called BioSmart, that can compute responses to arbitrary natural language queries over arbitrary databases once a syntactic classification of the natural language query is made

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good. Graphical abstrac

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good.Seventh Framework Programme (European Commission) (grant agreement No. 284709

Strategies for executing federated queries in SPARQL1.1

A common way for exposing RDF data on the Web is by means of SPARQL endpoints which allow end users and applications to query just the RDF data they want. However, servers hosting SPARQL endpoints often restrict access to the data by limiting the amount of results returned per query or the amount of queries per time that a client may issue. As this may affect query completeness when using SPARQL1.1's federated query extension, we analysed different strategies to implement federated queries with the goal to circumvent endpoint limits. We show that some seemingly intuitive methods for decomposing federated queries provide unsound results in the general case, and provide fixes or discuss under which restrictions these recipes are still applicable. Finally, we evaluate the proposed strategies for checking their feasibility in practice

Following carbon condensation by in-situ TEM : towards a rational understanding of the processes in the synthesis of nitrogen-doped carbonaceous materials.

Porous carbonaceous materials obtained from biomass have been an important class of CO2 sorbents since ancient times. Recent progress in carbon-based adsorbent technology is based on the implication of the concept of heteroatom doping. In this respect, the synthesis of carbonaceous materials through one-step condensation of cheap nitrogen-containing molecular precursors is an attractive strategy for obtaining such N-doped carbons. The design of the adsorbents obtained by this route relies on the careful adjustment of synthesis parameters, such as the temperature, the heating rate, and the atmosphere. However, in most cases, the latter's choice remains rather empirical due to the lack of a fundamental understanding of the condensation mechanism of molecular precursors. In this work, we followed the structural, morphological, and chemical evolution of a molecular precursor (uric acid) at the nanoscale using a combination of in-situ condensation inside a scanning transmission electron microscope with ex-situ analysis of the products of condensation at different temperatures, atmospheres, and heating rates, and correlate our findings with the CO2 sorption properties of the obtained materials. We showed that varying pressures and reaction rates result in particles with different porosity. The porosity of the surface of the particles during the early stages of condensation governs the subsequent release of volatiles and the development of a hierarchical pore structure. We found that synthesis in vacuum enables effective condensation at considerably low temperatures (500 °C). Using a higher heating rate (10 °C/min) suppresses structural ripening and preserves the optimal size of micropores, thus giving a CO2 uptake twice as high compared to samples synthesized in nitrogen atmosphere, which is commonly used, preserving the same selectivity.ER

A grid-based infrastructure for distributed retrieval

In large-scale distributed retrieval, challenges of latency, heterogeneity, and dynamicity emphasise the importance of infrastructural support in reducing the development costs of state-of-the-art solutions. We present a service-based infrastructure for distributed retrieval which blends middleware facilities and a design framework to ‘lift’ the resource sharing approach and the computational services of a European Grid platform into the domain of e-Science applications. In this paper, we give an overview of the DILIGENT Search Framework and illustrate its exploitation in the field of Earth Science

Just-In-Time Data Distribution for Analytical Query Processing

Distributed processing commonly requires data spread across machines using a priori static or hash-based data allocation. In this paper, we explore an alternative approach that starts from a master node in control of the complete database, and a variable number of worker nodes for delegated query processing. Data is shipped just-in-time to the worker nodes using a need to know policy, and is being reused, if possible, in subsequent queries. A bidding mechanism among the workers yields a scheduling with the most efficient reuse of previously shipped data, minimizing the data transfer costs. Just-in-time data shipment allows our system to benefit from locally available idle resources to boost overall performance. The system is maintenance-free and allocation is fully transparent to users. Our experiments show that the proposed adaptive distributed architecture is a viable and flexible alternative for small scale MapReduce-type of settings

Determining the impact regions of competing options in preference space

2017 ACM SIGMOD International Conference on Management of Data, SIGMOD 2017, Chicago, Illinois, USA, 14-19 May 2017In rank-aware processing, user preferences are typically represented by a numeric weight per data attribute, collectively forming a weight vector. The score of an option (data record) is defined as the weighted sum of its individual attributes. The highest-scoring options across a set of alternatives (dataset) are shortlisted for the user as the recommended ones. In that setting, the user input is a vector (equivalently, a point) in a d-dimensional preference space, where d is the number of data attributes. In this paper we study the problem of determining in which regions of the preference space the weight vector should lie so that a given option (focal record) is among the top-k score-wise. In effect, these regions capture all possible user profiles for which the focal record is highly preferable, and are therefore essential in market impact analysis, potential customer identification, profile-based marketing, targeted advertising, etc. We refer to our problem as k-Shortlist Preference Region identification (kSPR), and exploit its computational geometric nature to develop a framework for its efficient (and exact) processing. Using real and synthetic benchmarks, we show that our most optimized algorithm outperforms by three orders of magnitude a competitor we constructed from previous work on a different problem.Department of Computing2016-2017 > Academic research: refereed > Refereed conference paperbcw