Tightly-coupled spatial database features in the Odysseus/OpenGIS DBMS for high-performance

Conventional object-relational database management system (ORDBMS) vendors provide extension mechanisms for adding user-defined types and functions to their own DBMSs. Here, the extension mechanisms are implemented using a high-level (typically, SQL-level) interface. We call this mechanism loose-coupling. The advantage of loose-coupling is that it is easy to implement. However, it is not preferable for implementing new data types and operations in large databases when high performance is required. We have earlier proposed the tight-coupling architecture (Whang et al. 2002, 2005) to satisfy this requirement. In tight-coupling, new data types and operations are integrated into the core of the DBMS engine in the extensible type layer. Thus, they are supported in a consistent manner with high performance. This tight-coupling architecture is being used to incorporate information retrieval features and spatial database features into the Odysseus ORDBMS that has been under development at KAIST/AITrc for 19 years. In this paper, we introduce the tightly-coupled spatial database features of Odysseus/OpenGIS. By taking advantage of tight-coupling, Odysseus/OpenGIS provides excellent performance in processing spatial queries as well as flexible concurrency control and recovery on spatial data. We show the performance through extensive experiments. Finally, we present sample applications of a geographical information system (GIS) implemented using Odysseus/OpenGIS.     

Efficient and flexible access control via Jones-optimal logic program specialisation

We describe the use of a flexible meta-interpreter for performing access control checks on deductive databases. The meta-program is implemented in Prolog and takes as input a database and an access policy specification. For processing access control requests we specialise the meta-program for a given access policy and database by using the logen partial evaluation system. The resulting specialised control checking program is dependent solely upon dynamic information that can only be known at the time of actual access request evaluation. In addition to describing our approach, we give a number of performance measures for our implementation of an access control checker. In particular, we show that by using our approach we get flexible access control with virtually no overhead, satisfying the Jones optimality criterion. The paper also shows how to satisfy the Jones optimality criterion more generally for interpreters written in the non-ground representation.     

Predictable performance and high query concurrency for data analytics

Conventional data warehouses employ the query-at-a-time model, which maps each query to a distinct physical plan. When several queries execute concurrently, this model introduces contention and thrashing, because the physical plans??unaware of each other??compete for access to the underlying I/O and computation resources. As a result, while modern systems can efficiently optimize and evaluate a single complex data analysis query, their performance suffers significantly and can be highly erratic when multiple complex queries run at the same time. We present in this paper Cjoin, a new design that substantially improves throughput in large-scale data analytics systems processing many concurrent join queries. In contrast to the conventional query-at-a-time model our approach employs a single physical plan that shares I/O, computation, and tuple storage across all in-flight join queries. We use an ??always on?? pipeline of non-blocking operators, managed by a controller that continuously examines the current query mix and optimizes the pipeline on the fly. Our design enables data analytics engines to scale gracefully to large data sets, provide predictable execution times, and reduce contention. We implemented Cjoin as an extension to the PostgreSQL DBMS. This prototype outperforms conventional commercial systems by an order of magnitude for tens to hundreds of concurrent queries.     

A Parallel Dynamic Binary Translator for Efficient Multi-Core Simulation

In recent years multi-core processors have seen broad adoption in application domains ranging from embedded systems through general-purpose computing to large-scale data centres. Simulation technology for multi-core systems, however, lags behind and does not provide the simulation speed required to effectively support design space exploration and parallel software development. While state-of-the-art instruction set simulators (Iss) for single-core machines reach or exceed the performance levels of speed-optimised silicon implementations of embedded processors, the same does not hold for multi-core simulators where large performance penalties are to be paid. In this paper we develop a fast and scalable simulation methodology for multi-core platforms based on parallel and just-in-time (Jit) dynamic binary translation (Dbt). Our approach can model large-scale multi-core configurations, does not rely on prior profiling, instrumentation, or compilation, and works for all binaries targeting a state-of-the-art embedded multi-core platform implementing the ARCompact instruction set architecture (Isa). We have evaluated our parallel simulation methodology against the industry standard Splash-2 and Eembc MultiBench benchmarks and demonstrate simulation speeds up to 25,307 Mips on a 32-core x86 host machine for as many as 2,048 target processors whilst exhibiting minimal and near constant overhead, including memory considerations.     

Deciding floating-point logic with abstract conflict driven clause learning

We present a bit-precise decision procedure for the theory of floating-point arithmetic. The core of our approach is a non-trivial, lattice-theoretic generalisation of the conflict-driven clause learning algorithm in modern sat solvers to lattice-based abstractions. We use floating-point intervals to reason about the ranges of variables, which allows us to directly handle arithmetic and is more efficient than encoding a formula as a bit-vector as in current floating-point solvers. Interval reasoning alone is incomplete, and we obtain completeness by developing a conflict analysis algorithm that reasons natively about intervals. We have implemented this method in the mathsat5 smt solver and evaluated it on assertion checking problems that bound the values of program variables. Our new technique is faster than a bit-vector encoding approach on 80 % of the benchmarks, and is faster by one order of magnitude or more on 60 % of the benchmarks. The generalisation of cdcl we propose is widely applicable and can be used to derive abstraction-based smt solvers for other theories.     

Deriving performance-relevant infrastructure properties through model-based experiments with Ginpex

To predict the performance of an application, it is crucial to consider the performance of the underlying infrastructure. Thus, to yield accurate prediction results, performance-relevant properties and behaviour of the infrastructure have to be integrated into performance models. However, capturing these properties is a cumbersome and error-prone task, as it requires carefully engineered measurements and experiments. Existing approaches for creating infrastructure performance models require manual coding of these experiments, or ignore the detailed properties in the models. The contribution of this paper is the Goal-oriented INfrastructure Performance EXperiments (Ginpex) approach, which introduces goal-oriented and model-based specification and generation of executable performance experiments for automatically detecting and quantifying performance-relevant infrastructure properties. Ginpex provides a metamodel for experiment specification and comes with predefined experiment templates that provide automated experiment execution on the target platform and also automate the evaluation of the experiment results. We evaluate Ginpex using three case studies, where experiments are executed to quantify various infrastructure properties.     

Monitoring properties of analog and mixed-signal circuits

In this paper, we present a comprehensive overview of the property-based monitoring framework for analog and mixed-signal systems. Our monitoring approach is centered around the Signal Temporal Logic (Stl) specification language, and is implemented in a stand-alone monitoring tool (Amt). We apply this property-based methodology to two industrial case studies and briefly present some recent extensions of Stl that were motivated by practical needs of analog designers.     

A Speculative Parallel DFA Membership Test for Multicore,SIMD and Cloud Computing Environments

We present techniques to parallelize membership tests for Deterministic Finite Automata (DFAs). Our method searches arbitrary regular expressions by matching multiple bytes in parallel using speculation. We partition the input string into chunks, match chunks in parallel, and combine the matching results. Our parallel matching algorithm exploits structural DFA properties to minimize the speculative overhead. Unlike previous approaches, our speculation is failure-free, i.e., (1) sequential semantics are maintained, and (2) speed-downs are avoided altogether. On architectures with a SIMD gather-operation for indexed memory loads, our matching operation is fully vectorized. The proposed load-balancing scheme uses an off-line profiling step to determine the matching capacity of each participating processor. Based on matching capacities, DFA matches are load-balanced on inhomogeneous parallel architectures such as cloud computing environments. We evaluated our speculative DFA membership test for a representative set of benchmarks from the Perl-compatible Regular Expression (PCRE) library and the PROSITE protein database. Evaluation was conducted on a 4 CPU (40 cores) shared-memory node of the Intel Academic Program Manycore Testing Lab (Intel MTL), on the Intel AVX2 SDE simulator for 8-way fully vectorized SIMD execution, and on a 20-node (288 cores) cluster on the Amazon EC2 computing cloud. Obtained speedups are on the order of $\mathcal O \left( 1+\frac{|P|-1}{|Q|\cdot \gamma }\right) $ , where $|P|$ denotes the number of processors or SIMD units, $|Q|$ denotes the number of DFA states, and $0<\gamma \le 1$ represents a statically computed DFA property. For all observed cases, we found that $0.02<\gamma <0.47$ . Actual speedups range from 2.3 $\times $ to 38.8 $\times $ for up to 512 DFA states for PCRE, and between 1.3 $\times $ and 19.9 $\times $ for up to 1,288 DFA states for PROSITE on a 40-core MTL node. Speedups on the EC2 computing cloud range from 5.0 $\times $ to 65.8 $\times $ for PCRE, and from 5.0 $\times $ to 138.5 $\times $ for PROSITE. Speedups of our C-based DFA matcher over the Perl-based ScanProsite scan tool range from 559.3 $\times $ to 15079.7 $\times $ on a 40-core MTL node. We show the scalability of our approach for input-sizes of up to 10 GB.     

Outlier detection from large distributed databases

In this paper, we present an innovative system, coined as DISTROD (a.k.a DISTRibuted Outlier Detector), for detecting outliers, namely abnormal instances or observations, from multiple large distributed databases. DISTROD is able to effectively detect the so-called global outliers from distributed databases that are consistent with those produced by the centralized detection paradigm. DISTROD is equipped with a number of optimization/boosting strategies which empower it to significantly enhance its speed performance and reduce its communication overhead. Experimental evaluation demonstrates the good performance of DISTROD in terms of speed and communication overhead.     

Is it DSS or OLTP: automatically identifying DBMS workloads

The type of the workload on a database management system (DBMS) is a key consideration in tuning the system. Allocations for resources such as main memory can be very different depending on whether the workload type is Online Transaction Processing (OLTP) or Decision Support System (DSS). A DBMS also typically experiences changes in the type of workload it handles during its normal processing cycle. Database administrators must therefore recognize the significant shifts of workload type that demand reconfiguring the system in order to maintain acceptable levels of performance. We envision intelligent, autonomic DBMSs that have the capability to manage their own performance by automatically recognizing the workload type and then reconfiguring their resources accordingly. In this paper, we present an approach to automatically identifying a DBMS workload as either OLTP or DSS. Using data mining techniques, we build a classification model based on the most significant workload characteristics that differentiate OLTP from DSS and then use the model to identify any change in the workload type. We construct and compare classifiers built from two different sets of workloads, namely the TPC-C and TPC-H benchmarks and the Browsing and Ordering profiles from the TPC-W benchmark. We demonstrate the feasibility and success of these classifiers with TPC-generated workloads and with industry-supplied workloads.     

Asteroid: Scalable Online Memory Diagnostics for Multi-core,Multi-socket Servers

Memory diagnostics are important to improving the resilience of DRAM main memory. As bit cell size reaches physical limits, DRAM memory will be more likely to suffer both transient and permanent errors. Memory diagnostics that operate online can be a component of a comprehensive strategy to allay errors. This paper presents a novel approach, Asteroid, to integrate online memory diagnostics during workload execution. The approach supports diagnostics that adapt at runtime to workload behavior and resource availability to maximize test quality while reducing performance overhead. We describe Asteroid’s design and how it can be efficiently integrated with a hierarchical memory allocator in modern operating systems. We also present how the framework enables control policies to dynamically configure a diagnostic. Using an adaptive policy, in a 16-core server, Asteroid has modest overhead of 1–4 % for workloads with low to high memory demand. For these workloads, Asteroid’s adaptive policy has good error coverage and can thoroughly test memory.     

Approximation scheme for burst scheduling with minimum overhead in time slicing mobile TV

In this paper, we consider the problem of minimizing switching overhead of burst scheduling in time slicing mobile TV broadcast systems. This problem was formulated by Hsu and Hefeeda, and was given an elegant approximation scheme with an approximation ratio of at least two. Our proposed scheme significantly improves the approximation ratio of the previous scheme. More concretely, it generates a burst schedule for mobile TV systems whose switching overhead is at most \((1/\ln 2)+\epsilon \simeq 1.4427\) times of optimum, where \(\ln \) is the natural logarithm and \(\epsilon \) is arbitrary positive constant. Our scheme is a combination of a binary partion of burst cycles and a shifting method which is commonly used for the analysis of approximation algorithms designed for unit disk graphs.     

Cross-categorization of legal concepts across boundaries of legal systems: in consideration of inferential links

This work contrasts Giovanni Sartor’s view of inferential semantics of legal concepts (Sartor in Artif Intell Law 17:217–251, 2009) with a probabilistic model of theory formation (Kemp et al. in Cognition 114:165–196, 2010). The work further explores possibilities of implementing Kemp’s probabilistic model of theory formation in the context of mapping legal concepts between two individual legal systems. For implementing the legal concept mapping, we propose a cross-categorization approach that combines three mathematical models: the Bayesian Model of Generalization (BMG; Tenenbaum and Griffiths in Behav Brain Sci 4:629–640, 2001), the probabilistic model of theory formation, i.e., the Infinite Relational Model (IRM) first introduced by Kemp et al. (The twenty-first national conference on artificial intelligence, 2006, Cognition 114:165–196, 2010) and its extended model, i.e., the normal-IRM (n-IRM) proposed by Herlau et al. (IEEE International Workshop on Machine Learning for Signal Processing, 2012). We apply our cross-categorization approach to datasets where legal concepts related to educational systems are respectively defined by the Japanese- and the Danish authorities according to the International Standard Classification of Education. The main contribution of this work is the proposal of a conceptual framework of the cross-categorization approach that, inspired by Sartor (Artif Intell Law 17:217–251, 2009), attempts to explain reasoner’s inferential mechanisms.     

Instance-level worst-case query bounds on R-trees

Even with its significant impacts on the database area, the R-tree is often criticized by its lack of good worst-case guarantees. For example, in range search (where we want to report all the data points in a query rectangle), it is known that on adversely designed datasets and queries, an R-tree can be as slow as a sequential scan that simply reads all the data points. Nevertheless, R-trees work so well on real data that they have been widely implemented in commercial systems. This stark contrast has caused long-term controversy between practitioners and theoreticians as to whether this structure deserves its fame. This paper provides theoretical evidence that, somewhat surprisingly, R-trees are efficient in the worst case for range search on many real datasets. Given any integer \(K\) , we explain how to obtain an upper bound on the cost of answering all (i.e., infinitely many) range queries retrieving at most \(K\) objects. On practical data, the upper bound is only a fraction of the overhead of sequential scan (unless, apparently, \(K\) is at the same order as the dataset size). Our upper bounds are tight up to a constant factor, namely they cannot be lowered by more than \(O(1)\) times while still capturing the most expensive queries. Our upper bounds can be calculated in constant time by remembering only three integers. These integers, in turn, are generated from only the leaf MBRs of an R-tree, but not the leaf nodes themselves. In practice, the internal nodes are often buffered in memory, so that the integers aforementioned can be efficiently maintained along with the data updates and made available to a query optimizer at any time. Furthermore, our analytical framework introduces instance-level query bound as a new technique for evaluating the efficiency of heuristic structures in a theory-flavored manner (previously, experimentation was the dominant assessment method).     

Insensitizing exact controls for the scalar wave equation and exact controllability of 2-coupled cascade systems of PDE’s by a single control

We study the exact controllability, by a reduced number of controls, of coupled cascade systems of PDE’s and the existence of exact insensitizing controls for the scalar wave equation. We give a necessary and sufficient condition for the observability of abstract-coupled cascade hyperbolic systems by a single observation, the observation operator being either bounded or unbounded. Our proof extends the two-level energy method introduced in Alabau-Boussouira (Siam J Control Opt 42:871–906, 2003) and Alabau-Boussouira and Léautaud (J Math Pures Appl 99:544–576, 2013) for symmetric coupled systems, to cascade systems which are examples of non-symmetric coupled systems. In particular, we prove the observability of two coupled wave equations in cascade if the observation and coupling regions both satisfy the Geometric Control Condition (GCC) of Bardos et al. (SIAM J Control Opt 30:1024–1065, 1992). By duality, this solves the exact controllability, by a single control, of $2$ -coupled abstract cascade hyperbolic systems. Using transmutation, we give null-controllability results for the multidimensional heat and Schrödinger $2$ -coupled cascade systems under GCC and for any positive time. By our method, we can treat cases where the control and coupling coefficients have disjoint supports, partially solving an open question raised by de Teresa (CPDE 25:39–72, 2000). Moreover we answer the question of the existence of exact insensitizing locally distributed as well as boundary controls of scalar multidimensional wave equations, raised by Lions (Actas del Congreso de Ecuaciones Diferenciales y Aplicaciones (CEDYA), Universidad de Málaga, pp 43–54, 1989) and later on by Dáger (Siam J Control Opt 45:1758–1768, 2006) and Tebou (C R Acad Sci Paris 346(Sér I):407–412, 2008).     

gbase: an efficient analysis platform for large graphs

Graphs appear in numerous applications including cyber security, the Internet, social networks, protein networks, recommendation systems, citation networks, and many more. Graphs with millions or even billions of nodes and edges are common-place. How to store such large graphs efficiently? What are the core operations/queries on those graph? How to answer the graph queries quickly? We propose Gbase, an efficient analysis platform for large graphs. The key novelties lie in (1) our storage and compression scheme for a parallel, distributed settings and (2) the carefully chosen graph operations and their efficient implementations. We designed and implemented an instance of Gbase using Mapreduce/Hadoop. Gbase provides a parallel indexing mechanism for graph operations that both saves storage space, as well as accelerates query responses. We run numerous experiments on real and synthetic graphs, spanning billions of nodes and edges, and we show that our proposed Gbase is indeed fast, scalable, and nimble, with significant savings in space and time.     

Fault tolerance in distributed systems using fused state machines

Replication is a standard technique for fault tolerance in distributed systems modeled as deterministic finite state machines (DFSMs or machines). To correct \(f\) crash or \(\lfloor f/2 \rfloor \) Byzantine faults among \(n\) different machines, replication requires \(nf\) backup machines. We present a solution called fusion that requires just \(f\) backup machines. First, we build a framework for fault tolerance in DFSMs based on the notion of Hamming distances. We introduce the concept of an ( \(f\) , \(m\) )-fusion, which is a set of \(m\) backup machines that can correct \(f\) crash faults or \(\lfloor f/2 \rfloor \) Byzantine faults among a given set of machines. Second, we present an algorithm to generate an ( \(f\) , \(f\) )-fusion for a given set of machines. We ensure that our backups are efficient in terms of the size of their state and event sets. Third, we use locality sensitive hashing for the detection and correction of faults that incurs almost the same overhead as that for replication. We detect Byzantine faults with time complexity \(O(n f)\) on average while we correct crash and Byzantine faults with time complexity \(O(n \rho f)\) with high probability, where \(\rho \) is the average state reduction achieved by fusion. Finally, our evaluation of fusion on the widely used MCNC’91 benchmarks for DFSMs shows that the average state space savings in fusion (over replication) is 38 % (range 0–99 %). To demonstrate the practical use of fusion, we describe its potential application to two areas: sensor networks and the MapReduce framework. In the case of sensor networks a fusion-based solution can lead to significantly fewer sensor-nodes than a replication-based solution. For the MapReduce framework, fusion can reduce the number of map-tasks compared to replication. Hence, fusion results in considerable savings in state space and other resources such as the power needed to run the backups.     

The ontological key: automatically understanding and integrating forms to access the deep Web

Forms are our gates to the Web. They enable us to access the deep content of Web sites. Automatic form understanding provides applications, ranging from crawlers over meta-search engines to service integrators, with a key to this content. Yet, it has received little attention other than as component in specific applications such as crawlers or meta-search engines. No comprehensive approach to form understanding exists, let alone one that produces rich models for semantic services or integration with linked open data. In this paper, we present opal, the first comprehensive approach to form understanding and integration. We identify form labeling and form interpretation as the two main tasks involved in form understanding. On both problems, opal advances the state of the art: For form labeling, it combines features from the text, structure, and visual rendering of a Web page. In extensive experiments on the ICQ and TEL-8 benchmarks and a set of 200 modern Web forms, opal outperforms previous approaches for form labeling by a significant margin. For form interpretation, opal uses a schema (or ontology) of forms in a given domain. Thanks to this domain schema, it is able to produce nearly perfect ( $>$ > 97 % accuracy in the evaluation domains) form interpretations. Yet, the effort to produce a domain schema is very low, as we provide a datalog-based template language that eases the specification of such schemata and a methodology for deriving a domain schema largely automatically from an existing domain ontology. We demonstrate the value of opal’s form interpretations through a light-weight form integration system that successfully translates and distributes master queries to hundreds of forms with no error, yet is implemented with only a handful translation rules.     

A Competitive Analysis for Balanced Transactional Memory Workloads

We consider transactional memory contention management in the context of balanced workloads, where if a transaction is writing, the number of write operations it performs is a constant fraction of its total reads and writes. We explore the theoretical performance boundaries of contention management in balanced workloads from the worst-case perspective by presenting and analyzing two new polynomial time contention management algorithms. We analyze the performance of a contention management algorithm by comparison with an optimal offline contention management algorithm to provide a competitive ratio. The first algorithm Clairvoyant is $O(\sqrt{s})$ -competitive, where s is the number of shared resources. This algorithm depends on explicitly knowing the conflict graph at each time step of execution. The second algorithm Non-Clairvoyant is $O(\sqrt{s} \cdot \log n)$ -competitive, with high probability, which is only a O(log?n) factor worse, but does not require knowledge of the conflict graph, where n is the number of transactions. Both of these algorithms are greedy. We also prove that the performance of Clairvoyant is close to optimal, since there is no polynomial time contention management algorithm for the balanced transaction scheduling problem that is better than $O((\sqrt{s})^{1-\varepsilon})$ -competitive for any constant ε>0, unless NP?ZPP. To our knowledge, these results are significant improvements over the best previously known O(s) competitive ratio bound.     

Activity representation with motion hierarchies

Complex activities, e.g. pole vaulting, are composed of a variable number of sub-events connected by complex spatio-temporal relations, whereas simple actions can be represented as sequences of short temporal parts. In this paper, we learn hierarchical representations of activity videos in an unsupervised manner. These hierarchies of mid-level motion components are data-driven decompositions specific to each video. We introduce a spectral divisive clustering algorithm to efficiently extract a hierarchy over a large number of tracklets (i.e. local trajectories). We use this structure to represent a video as an unordered binary tree. We model this tree using nested histograms of local motion features. We provide an efficient positive definite kernel that computes the structural and visual similarity of two hierarchical decompositions by relying on models of their parent–child relations. We present experimental results on four recent challenging benchmarks: the High Five dataset (Patron-Perez et al., High five: recognising human interactions in TV shows, 2010), the Olympics Sports dataset (Niebles et al., Modeling temporal structure of decomposable motion segments for activity classification, 2010), the Hollywood 2 dataset (Marszalek et al., Actions in context, 2009), and the HMDB dataset (Kuehne et al., HMDB: A large video database for human motion recognition, 2011). We show that per-video hierarchies provide additional information for activity recognition. Our approach improves over unstructured activity models, baselines using other motion decomposition algorithms, and the state of the art.