The like regular expression predicate has been part of the SQL standard since at least 1989. However, despite its popularity and wide usage, database vendors provide only limited indexing support for regular expression queries which almost always require a full table scan. In this paper we propose a rigorous and robust approach for providing indexing support for regular expression queries. Our approach consists of formulating the indexing problem as a combinatorial optimization problem. We begin with a database, abstracted as a collection of strings. From this data set we generate a query workload. The input to the optimization problem is the database and the workload. The output is a set of multigrams (substrings) which can be used as keys to records which satisfy the query workload. The multigrams can then be integrated with the data structure (like B+ trees) to provide indexing support for the queries. We provide a deterministic and a randomized approximation algorithm (with provable guarantees) to solve the optimization problem. Extensive experiments on synthetic data sets demonstrate that our approach is accurate and efficient. We also present a case study on PROSITE patterns - which are complex regular expression signatures for classes of proteins. Again, we are able to demonstrate the utility of our indexing approach in terms of accuracy and efficiency. Thus, perhaps for the first time, there is a robust and practical indexing mechanism for an important class of database queries.
Hore formalizes the multigram selection problem as an instance of the Budgeted Maximum Coverage (BMC) @cite_5 problem. Specifically the cover set forms the Budgeted' part while the utility of the index is captured in the Coverage' component. The main principle of the BEST algorithm is to select multigrams which increase the index hit rate so more queries can utilize the algorithm. However, a major weakness of the BEST algorithm is that it neither scales to large datasets nor large query workloads because it uses a cartesian product of query workload and database as the search space.
@cite_5
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In a minimum partial set multi-cover problem (MinPSMC), given an element set E, a collection of subsets ( S 2^E ), a cost (w_S ) on each set (S S ), a covering requirement (r_e ) for each element (e E ), and an integer k, the goal is to find a sub-collection ( F S ) to fully cover at least k elements such that the cost of ( F ) is as small as possible, where element e is fully covered by ( F ) if it belongs to at least (r_e ) sets of ( F ). On the application side, the problem has its background in the seed selection problem in a social network. On the theoretical side, it is a natural combination of the minimum partial (single) set cover problem (MinPSC) and the minimum set multi-cover problem (MinSMC). Although both MinPSC and MinSMC admit good approximations whose performance ratios match those lower bounds for the classic set cover problem, previous studies show that theoretical study on MinPSMC is quite challenging. In this paper, we prove that MinPSMC cannot be approximated within factor (O(n^ 1 2( n)^c ) ) under the ETH assumption. A primal dual algorithm for MinPSMC is presented with a guaranteed performance ratio (O( n ) ) when (r_ ) and f are constants, where (r_ = _ e E r_e ) is the maximum covering requirement and f is the maximum frequency of elements (that is the maximum number of sets containing a common element). We also improve the ratio for a restricted version of MinPSMC which possesses a graph-type structure. In this paper, we study the minimum partial set multi-cover problem (PSMC). Given an element set E, a collection of subsets ( S 2^E ), a cost (c_S ) on each set (S S ), a covering requirement (r_e ) for each element (e E ), and an integer k, the PSMC problem is to find a sub-collection ( F S ) to fully cover at least k elements such that the cost of ( F ) is as small as possible, where element e is fully covered by ( F ) if it belongs to at least (r_e ) sets of ( F ). This paper presents an approximation algorithm using local ratio method achieving performance ratio ( k ( 1 f-r_ + r_ r_ ) , 1 + f r_ + 1 r_ - 1 r_ -1, 1 ), where ( ) is the size of a maximum set, f is the maximum number of sets containing a common element, ( ) is the minimum percentage of elements required to be fully covered during iterations of the algorithm, and (r_ ) and (r_ ) are the maximum and the minimum covering requirement, respectively. In particular, when (r_ ) is a constant, the first term can be omitted. Notice that our ratio coincides with the classic ratio f for both the set multi-cover problem (in which case (k=|E| )) and the partial set single-cover problem (in which case (r_ =1 )). However, when (k 1 ), the ratio might be as large as ( (n) ). This result shows an interesting “shock wave like” feature of approximating PSMC. The purpose of this paper is trying to arouse some interest in such a feature and attract more work on this challenging problem. We consider a Multi-Sender Unicast Index-Coding (MSUIC) problem, where in a broadcast network, multiple senders collaboratively send distinct messages to multiple receivers, each having some subset of the messages a priori . The aim is to find the shortest index code that minimizes the total number of coded bits sent by the senders. In this paper, built on the classic single-sender minrank concept, we develop a new rank-minimization framework for MSUIC that explicitly takes into account the sender message constraints and minimizes the sum of the ranks of encoding matrices subject to the receiver decoding requirements. This framework provides a systematic way to construct multi-sender linear index codes and to study their capability in achieving the shortest index codelength per message length (i.e., the optimal broadcast rate). In particular, we establish the optimal broadcast rate for all critical MSUIC instances with up to four receivers and show that a binary linear index code is optimal for all, except 15 instances with four receivers. We also propose a heuristic algorithm (in lieu of exhaustive search) to solve the rank-minimization problem. The effectiveness of the algorithm is validated by numerical studies of MSUIC instances with four or more receivers. Tasks such as record linkage and multi-target tracking, which involve reconstructing the set of objects that underlie some observed data, are particularly challenging for probabilistic inference. Recent work has achieved efficient and accurate inference on such problems using Markov chain Monte Carlo (MCMC) techniques with customized proposal distributions. Currently, implementing such a system requires coding MCMC state representations and acceptance probability calculations that are specific to a particular application. An alternative approach, which we pursue in this paper, is to use a general-purpose probabilistic modeling language (such as BLOG) and a generic Metropolis-Hastings MCMC algorithm that supports user-supplied proposal distributions. Our algorithm gains flexibility by using MCMC states that are only partial descriptions of possible worlds; we provide conditions under which MCMC over partial worlds yields correct answers to queries. We also show how to use a context-specific Bayes net to identify the factors in the acceptance probability that need to be computed for a given proposed move. Experimental results on a citation matching task show that our general-purpose MCMC engine compares favorably with an application-specific system. We critically evaluate the current state of research in multiple query opGrnization, synthesize the requirements for a modular opCrnizer, and propose an architecture. Our objective is to facilitate future research by providing modular subproblems and a good general-purpose data structure. In rhe context of this archiuzcture. we provide an improved subsumption algorithm. and discuss migration paths from single-query to multiple-query oplimizers. The architecture has three key ingredients. First. each type of work is performed at an appropriate level of abstraction. Segond, a uniform and very compact representation stores all candidate strategies. Finally, search is handled as a discrete optimization problem separable horn the query processing tasks. 1. Problem Definition and Objectives A multiple query optimizer (h4QO) takes several queries as input and seeks to generate a good multi-strategy, an executable operator gaph that simultaneously computes answers to all the queries. The idea is to save by evaluating common subexpressions only once. The commonalities to be exploited include identical selections and joins, predicates that subsume other predicates, and also costly physical operators such as relation scans and SOULS. The multiple query optimization problem is to find a multi-strategy that minimizes the total cost (with overlap exploited). Figure 1 .l shows a multi-strategy generated exploiting commonalities among queries Ql-Q3 at both the logical and physical level. To be really satisfactory, a multi-query optimization algorithm must offer solution quality, ejjiciency, and ease of Permission to copy without fee all a part of this mataial is granted provided that the copies are nut made a diitributed for direct commercial advantage, the VIDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Da Blse Endowment. To copy otherwise. urturepublim identify many kinds of commonalities (e.g., by predicate splitting, sharing relation scans); and search effectively to choose a good combination of l-strategies. Efficiency requires that the optimization avoid a combinatorial explosion of possibilities, and that within those it considers, redundant work on common subexpressions be minimized. ‘Finally, ease of implementation is crucial an algorithm will be practically useful only if it is conceptually simple, easy to attach to an optimizer, and requires relatively little additional soft- We study the index selection problem: Given a workload consisting of SQL statements on a database, and a user-specified storage constraint, recommend a set of indexes that have the maximum benefit for the given workload. We present a formal statement for this problem and show that it is computationally "hard" to solve or even approximate it. We develop a new algorithm for the problem which is based on treating the problem as a knapsack problem. The novelty of our approach lies in an LP (linear programming) based method that assigns benefits to individual indexes. For a slightly modified algorithm, that does more work, we prove that we can give instance specific guarantees about the quality of our solution. We conduct an extensive experimental evaluation of this new heuristic and compare it with previous solutions. Our results demonstrate that our solution is more scalable while achieving comparable quality. Bayesian approaches to utility elicitation typically adopt (myopic) expected value of information (EVOI) as a natural criterion for selecting queries. However, EVOI-optimization is usually computationally prohibitive. In this paper, we examine EVOI optimization using choice queries, queries in which a user is ask to select her most preferred product from a set. We show that, under very general assumptions, the optimal choice query w.r.t. EVOI coincides with the optimal recommendation set, that is, a set maximizing the expected utility of the user selection. Since recommendation set optimization is a simpler, submodular problem, this can greatly reduce the complexity of both exact and approximate (greedy) computation of optimal choice queries. We also examine the case where user responses to choice queries are error-prone (using both constant and mixed multinomial logit noise models) and provide worst-case guarantees. Finally we present a local search technique for query optimization that works extremely well with large outcome spaces. Abstract Index selection for relational databases is an important issue which has been researched quite extensively [1–5]. In the literature, in index selection algorithms for relational databases, at most one index is considered as a candidate for each attribute of a relation. However, it is possible that more than one different type of indexes with different storage space requirements may be present as candidates for an attribute. Also, it may not be possible to eliminate locally all but one of the candidate indexes for an attribute due to different benefits and storage space requirements associated with the candidates. Thus, the algorithms available in the literature for optimal index selection may not be used when there are multiple candidates for each attribute and there is a need for a global optimization algorithm in which at most one index can be selected from a set of candidate indexes for an attribute. The problem of index selection in the presence of multiple candidate indexes for each attribute (which we call the multiple choice index selection problem) has not been addressed in the literature. In this paper, we present the multiple choice index selection problem, show that it is NP-hard, and present an algorithm which gives an approximately optimal solution within a user specified error bound in a logarithmic time order. A problem of considerable interest in the design of a database is the selection of indexes. In this paper, we present a probabilistic model of transactions (queries, updates, insertions, and deletions) to a file. An evaluation function, which is based on the cost saving (in terms of the number of page accesses) attributable to the use of an index set, is then developed. The maximization of this function would yield an optimal set of indexes. Unfortunately, algorithms known to solve this maximization problem require an order of time exponential in the total number of attributes in the file. Consequently, we develop the theoretical basis which leads to an algorithm that obtains a near optimal solution to the index selection problem in polynomial time. The theoretical result consists of showing that the index selection problem can be solved by solving a properly chosen instance of the knapsack problem. A theoretical bound for the amount by which the solution obtained by this algorithm deviates from the true optimum is provided. This result is then interpreted in the light of evidence gathered through experiments. This paper presents Markov chain Monte Carlo data association (MCMCDA) for solving data association problems arising in multitarget tracking in a cluttered environment. When the number of targets is fixed, the single-scan version of MCMCDA approximates joint probabilistic data association (JPDA). Although the exact computation of association probabilities in JPDA is NP-hard, we prove that the single-scan MCMCDA algorithm provides a fully polynomial randomized approximation scheme for JPDA. For general multitarget tracking problems, in which unknown numbers of targets appear and disappear at random times, we present a multi-scan MCMCDA algorithm that approximates the optimal Bayesian filter. We also present extensive simulation studies supporting theoretical results in this paper. Our simulation results also show that MCMCDA outperforms multiple hypothesis tracking (MHT) by a significant margin in terms of accuracy and efficiency under extreme conditions, such as a large number of targets in a dense environment, low detection probabilities, and high false alarm rates. Current high-quality object detection approaches use the scheme of salience-based object proposal methods followed by post-classification using deep convolutional features. This spurred recent research in improving object proposal methods. However, domain agnostic proposal generation has the principal drawback that the proposals come unranked or with very weak ranking, making it hard to trade-off quality for running time. This raises the more fundamental question of whether high-quality proposal generation requires careful engineering or can be derived just from data alone. We demonstrate that learning-based proposal methods can effectively match the performance of hand-engineered methods while allowing for very efficient runtime-quality trade-offs. Using the multi-scale convolutional MultiBox (MSC-MultiBox) approach, we substantially advance the state-of-the-art on the ILSVRC 2014 detection challenge data set, with @math mAP for a single model and @math mAP for an ensemble of two models. MSC-Multibox significantly improves the proposal quality over its predecessor MultiBox method: AP increases from @math to @math for the ILSVRC detection challenge. Finally, we demonstrate improved bounding-box recall compared to Multiscale Combinatorial Grouping with less proposals on the Microsoft-COCO data set. Despite having various attractive qualities such as high prediction accuracy and the ability to quantify uncertainty and avoid over-fitting, Bayesian Matrix Factorization has not been widely adopted because of the prohibitive cost of inference. In this paper, we propose a scalable distributed Bayesian matrix factorization algorithm using stochastic gradient MCMC. Our algorithm, based on Distributed Stochastic Gradient Langevin Dynamics, can not only match the prediction accuracy of standard MCMC methods like Gibbs sampling, but at the same time is as fast and simple as stochastic gradient descent. In our experiments, we show that our algorithm can achieve the same level of prediction accuracy as Gibbs sampling an order of magnitude faster. We also show that our method reduces the prediction error as fast as distributed stochastic gradient descent, achieving a 4.1 improvement in RMSE for the Netflix dataset and an 1.8 for the Yahoo music dataset. Abstract This paper proposes a parallel hurricane optimization algorithm (PHOA) for solving economic emission load dispatch (EELD) problem in modern power systems. In PHOA, several sub-populations moving independently in the search space with the aim of simultaneously optimizing the problem objectives considering the local behavior between sub-populations. By this way, it is intended to search for the Pareto optimal solutions which are contrasting to the single optimal solution. The inherent characteristics of parallelization strategy can enhance the Pareto solutions and increase the convergence to reach the Pareto optimal solutions. Simulations are conducted on three test systems and comparisons with other optimization techniques that reported in the literature are demonstrated. The obtained results demonstrate the superiority of the proposed PHOA compared to other optimization techniques. Additional economic benefits with secure settings are fulfilled, while preserving all system constraints within their permissible limits. Added to that, two security indices are proposed from generation units and transmission lines. The highest security index from generation units reflects that the operating condition achieves more power reserve. In transmission lines, the highest security index means that the transmission lines operated beyond their congestion limits. For justification of the proposed security indices, the proposed solution methodology is employed to assure their benefits in terms of economical and environmental issues. The proposed algorithm improves the economic issue as well as enhances the power system operation in the technical point of view with acceptable levels of emissions. Moreover, design of experiments using the Taguchi approach is employed to calibrate the parameters of the algorithms. So, it can be considered as a promising alternative algorithm for solving problems in practical large-scale power systems. Due to the limited diffracting ability of millimeter wave (mm-wave) signals, solving the blockage problem in 60GHz WLANs is one of major challenges. Since multiple access points (AP) are typically employed in the indoor environment to ensure the coverage and to meet the increasing demand for extremely high data rate applications, in this paper, we propose a multi-AP architecture with which a MAC layer device called an Access Controller (AC) is employed to enable each station to associate and cooperate with multiple APs. In this way, multi-AP diversity can be exploited to solve the blockage problem. Since APs may not fully obtain the state of channels, we formulate the AP selection problem as a Partially Observed Markov Decision Process (POMDP) and obtain an optimal policy. Moreover, a threshold-based policy with reduced complexity is developed with which the decision for AP selection depends only on the number of consecutive transmission failures. The optimal threshold value for AP selection is also derived. Simulation results show that the threshold-based policy achieves almost the same performance as the optimal policy derived from POMDP. A natural way to deal with multiple, partially conflicting objectives is turning all the objectives but one into budget constraints. Many classical optimization problems, such as maximum spanning tree and forest, shortest path, maximum weight (perfect) matching, maximum weight independent set (basis) in a matroid or in the intersection of two matroids, become NP-hard even with one budget constraint. Still, for most of these problems efficient deterministic and randomized approximation schemes are known. Not much is known however about the case of two or more budgets: filling this gap, at least partially, is the main goal of this paper. In more detail, we obtain the following main results: Using iterative rounding for the first time in multi-objective optimization, we obtain multi-criteria PTASs (which slightly violate the budget constraints) for spanning tree, matroid basis, and bipartite matching with @math k = O ( 1 ) budget constraints. We present a simple mechanism to transform multi-criteria approximation schemes into pure approximation schemes for problems whose feasible solutions define an independence system. This gives improved algorithms for several problems. In particular, this mechanism can be applied to the above bipartite matching algorithm, hence obtaining a pure PTAS. We show that points in low-dimensional faces of any matroid polytope are almost integral, an interesting result on its own. This gives a deterministic approximation scheme for @math k -budgeted matroid independent set. We present a deterministic approximation scheme for @math k -budgeted matching (in general graphs), where @math k = O ( 1 ) . Interestingly, to show that our procedure works, we rely on a non-constructive result by Stromquist and Woodall, which is based on the Ham Sandwich Theorem. The theoretically optimal approach to multisensor-multitarget detection, tracking, and identification is a suitable generalization of the recursive Bayes nonlinear filter. Even in single-target problems, this optimal filter is so computationally challenging that it must usually be approximated. Consequently, multitarget Bayes filtering will never be of practical interest without the development of drastic but principled approximation strategies. In single-target problems, the computationally fastest approximate filtering approach is the constant-gain Kalman filter. This filter propagates a first-order statistical moment - the posterior expectation - in the place of the posterior distribution. The purpose of this paper is to propose an analogous strategy for multitarget systems: propagation of a first-order statistical moment of the multitarget posterior. This moment, the probability hypothesis density (PHD), is the function whose integral in any region of state space is the expected number of targets in that region. We derive recursive Bayes filter equations for the PHD that account for multiple sensors, nonconstant probability of detection, Poisson false alarms, and appearance, spawning, and disappearance of targets. We also show that the PHD is a best-fit approximation of the multitarget posterior in an information-theoretic sense. Achieving the boundary of the capacity region in the multiple-input multiple-output (MIMO) broadcast channel requires the use of dirty paper coding (DPC). As practical nearly optimum implementations of DPC are computationally complex, purely linear approaches are often used instead. However, in this case, the problem of maximizing a weighted sum rate constitutes a nonconvex and, in most cases, also a combinatorial optimization problem. In this paper, we present two heuristic nearly optimum algorithms with reduced computational complexity. For this purpose, a lower bound for the weighted sum rate under linear zero-forcing constraints is used. Based on this bound, both greedy algorithms successively allocate data streams to users. In each step, the user is determined that is given an additional data stream such that the increase in weighted sum rate becomes maximum. Thereby, the data stream allocations and filters obtained in the previous steps are kept fixed and only the filter corresponding to the additional data stream is optimized. The first algorithm determines the receive and transmit filters directly in the downlink. The other algorithm operates in the dual uplink, from which the downlink transmit and receive filters can be obtained via the general rate duality leading to nonzero-forcing in the downlink. Simulation results reveal marginal performance losses compared to more complex algorithms. In a STACS 2003 paper, Talwar analyzes the overpayment the VCG mechanism incurs for ensuring truthfulness in auctions. Among other results, he studies k-Set Cover (given a universe U and a collection of sets S 1 , S 2 , ? , S m , each having a cost c ( S i ) and at most k elements of U, find a minimum cost subcollection - a cover - whose union equals U) and shows that the payment of the VCG mechanism is at most k ? c ( OPT ' ) , where OPT ' is the best cover disjoint from the optimum cover OPT . The VCG mechanism requires finding an optimum cover. For k ? 3 , k-Set Cover is known to be NP-hard, and thus truthful mechanisms based on approximation algorithms are desirable. We show that the payment incurred by two mechanisms based on approximation algorithms (including the Greedy algorithm) is bounded by ( k - 1 ) c ( OPT ) + k ? c ( OPT ' ) . The same approximation algorithms have payment bounded by k ( c ( OPT ) + c ( OPT ' ) ) when applied to more general set systems, which include k-Polymatroid Cover, a problem related to Steiner Tree computations. If q is such that an element in a k-Set Cover instance appears in at most q sets, we show that the total payment based on our algorithms is bounded by q ? k 2 times the total payment of the VCG mechanism. Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness. The optimization of queries in distributed database systems is known to be subject to delicate trade-offs. For example, the Mariposa database system allows users to specify a desired delay-cost tradeoff (that is, to supply a decreasing function u(d), specifying how much the user is willing to pay in order to receive the query results within time d); Mariposa divides a query graph into horizontal “strides,” analyzes each stride, and uses a greedy heuristic to find the “best” plan for all strides. We show that Mariposa's greedy heuristic can be arbitrarily far from the desired optimum. Applying a recent approach in multiobjective optimization algorithms to this problem, we show that the optimum cost-delay trade-off (Pareto) curve in Mariposa's framework can be approximated fast within any desired accuracy. We also present a polynomial algorithm for the general multiobjective query optimization problem, which approximates arbirarily well the optimum cost-delay tradeoff (without the restriction of Mariposa's heuristic stride subdivision).