We design and develop an extensible model and a decision guidance system for making actionable recommendations on investments in heterogeneous infrastructure service networks. The model expresses the cash flows, as well as performance indicators, such as total cost of ownership and carbon emissions, as a function of both investment and operational controls within physical constraints of heterogeneous infrastructures and of balancing resource flows. Uniquely, it is designed to make Pareto-optimal investment decisions under the assumption of optimal operational controls over the time horizon. We also develop an extensible library of domain-specific operational analytic models for infrastructure components, initially for desalination and water systems, including pumps, renewable energy sources, water and power storage, and Reverse Osmosis desalination units. Finally, we conduct and report on a feasibility study for this domain to demonstrate the ability to solve realistic size problems.

This paper reports on the development of a model of COVID-19 transmission dynamics that takes into account a comprehensive mitigation protocol. This is necessary for public health decision support and making actionable recommendations on COVID-19 response. The comprehensive mitigation protocol includes (1) personal protection and social distancing, (2) use of smart applications for symptom reporting and contact tracing, (3) targeted testing based on identification of individuals with possible exposure and/or infection via symptom reporting and contact tracing, (4) surveillance testing, and (5) shelter, quarantine and isolation procedures. The proposed model (1) extends a common epidemiological discrete dynamic model with the comprehensive mitigation protocol, (2) uses Bayesian probability analysis to estimate the conditional probabilities of being in non-circulating epidemiological sub-compartments as a function of the mitigation protocol parameters, based on which it (3) estimates transition ratios among the compartments, and (4) computes a range of key performance indicators including health outcomes, mitigation cost and productivity loss. The proposed model can serve as a critical component for COVID-19 mitigation decision support and recommender systems, as part of a broader effort to support urgent pandemic response.

In this paper we report on the development of a software framework and system for composition, optimization and trade-off analysis of manufacturing and contract service networks based on a reusable repository of performance models. Performance models formally describe process feasibility constraints and metrics of interest, such as cost, throughput and CO2emissions, as a function of fixed and control parameters, such as equipment and contract properties and settings. The repository contains performance models for (1) unit manufacturing processes, (2) base contract services, and (3) a composite steady-state service network. The proposed framework allows process engineers to (1) hierarchically compose model instances of service networks, which can represent production cells, lines, factory facilities and supply chains, and (2) perform deterministic optimization based on mathematical programming and Pareto-optimal trade-off analysis. We case study the framework on a service network for a heat sink product which involves contract vendors and manufacturers, unit manufacturing process services including cutting/shearing and Computer Numerical Control (CNC) machining with milling and drilling steps, quality inspection, finishing and assembly.

In this paper we report on the development of a system for managing a repository and conducting analysis and optimization on manufacturing performance models. The repository is designed to contain (1) unit manufacturing process performance models, (2) composite performance models representing production cells, lines, and facilities, (3) domain specific analytical views, and (4) ontologies and taxonomies. Initial implementation includes performance models for milling and drilling as well as a composite performance model for machining. These performance models formally capture (1) the metrics of energy consumption, CO2emissions, tool wear, and cost as a function of process controls and parameters, and (2) the process feasibility constraints. The initial scope of the system includes (1) an Integrated Development Environment and its interface, and (2) simulation and deterministic optimization of performance models through the use of Unity Decision Guidance Management System.

In this paper, we propose an architectural design and software framework for fast development of descriptive, diagnostic, predictive, and prescriptive analytics solutions for dynamic production processes. The proposed architecture and framework will support the storage of modular, extensible, and reusable Knowledge Base (KB) of process performance models. The approach requires the development of automatic methods that can translate the high-level models in the reusable KB into low-level specialized models required by a variety of underlying analysis tools, including data manipulation, optimization, statistical learning, estimation, and simulation. We also propose an organization and key structure for the reusable KB, composed of atomic and composite process performance models and domain-specific dashboards. Furthermore, we illustrate the use of the proposed architecture and framework by performing diagnostic tasks on a composite performance model.

This paper is focused on decision analytics for smart manufacturing. We consider temporal manufacturing processes with stochastic throughput and inventories. We demonstrate the use of the recently proposed concept of the decision guidance analytics language to perform monitoring, analysis, planning, and execution tasks. To support these tasks we define the structure of and develop modular reusable process component models, which represent data, decision/control variables, computation of functions, constraints, and uncertainty. The tasks are then implemented by posing declarative queries of the decision guidance analytics language for data manipulation, what-if prediction analysis, decision optimization, and machine learning.

Renewable energy sources such as solar and wind are becoming increasingly prevalent for the energy mix of almost every country. However, this kind of energy is non-dispatch able, i.e., The supply can be interrupted and is usually not adjustable as in regular fossil-fueled power plants. The integration of these renewable energy sources in the Smart Grid (SG) has a large impact on the grid operations because of the variability and uncertainty of the supply. In addition to the increased criticality of the grid intermittence management, the SG must be able to manage this type of interruption of these energy sources effectively. Energy storage solutions such as batteries are not well developed yet and do not provide long term cost effective solutions. This paper provides a mathematical modeling approach to compensate for the intermittence of the supply from renewable energy sources for the SG. The idea is suitable in case of the integration of power generation with desalination production. The desalination plants can be used as a deferrable load to mitigate for the supply interruption. The optimal solution is based on a mathematical programming model to find the optimal scheduling of production and storage of desalinated water over the planning horizon such that the solution is not just feasible but also economically optimal. The real-world case study explains the advantages gained when applying the proposed approach.

Studied in this paper is the problem of restoring compromised privacy for micro-data disclosure with multiple disclosed views. The property of γ-privacy is proposed, which requires that the probability of an individual to be associated with a sensitive value must be bounded by γ in a possible table which is randomly selected from a set of tables that would lead the same disclosed answers. For the restricted case of a single disclosed view, the γ-privacy is shown to be equivalent to recursive ([EQUATION], 2)-Diversity, which is not defined for multiple disclosed views. The problem of deciding on γ-privacy for a set of disclosed views is proven to be #P-complete. To mitigate the high computational complexity, the property of γ-privacy is relaxed to be satisfied with (ε, θ) confidence, i.e., that the probability of disclosing a sensitive value of an individual must be bounded by γ + ε with statistical confidence θ. A Monte Carlo-based algorithm is proposed to check the relaxed property in O((λλ')4) time for constant ε and θ, where λ is the number of tuples in the original table and λ' is the number different sensitive values in the original table. Restoring compromised privacy using additional disclosed views is studied. Heuristic polynomial time algorithms are proposed based on enumerating and checking additional disclosed views. A preliminary experimental study is conducted on real-life medical data, which demonstrates that the proposed polynomial algorithms restore privacy in up to 60% of compromised disclosures.

Core Java is a framework which extends the programming language Java with built-in regression analysis, i.e., the capability to do parameter estimation for a function. Core Java is unique in that functional forms for regression analysis are expressed as first-class citizens, i.e., as Java programs, in which some parameters are not a priori known, but need to be learned from training sets provided as input. Typical applications of Core Java include calibration of parameters of computational processes, described as OO programs. If-then-else statements of Java language are naturally adopted to create piecewise functional forms of regression. Thus, minimization of the sum of least squared errors involves an optimization problem with a search space that is exponential to the size of learning set. In this paper, we propose a combinatorial restructuring algorithm which guarantees learning optimality and furthermore reduces the search space to be polynomial in the size of learning set, but exponential to the number of piece-wise bounds.

The demonstrated, high-level decisions query language DQL combines the decision optimization capability of mathematical programming and the data manipulation capability of traditional database query languages. DQL benefits application developers in two aspects. First, it avoids a conceptual impedance mismatch between mathematical programming and data access and makes decision optimization functionality readily accessible to database programmers with no prior experience in operations research. Second, a tight integration provides unique opportunities for more efficient evaluation as compared to a loosely coupled system. This demonstration uses an emergency response scenario to illustrate the power of the language and its implementation.

Proposed and implemented is the language CoReJava (constraint optimization regression in Java), which extends the programming language Java with regression analysis, i.e., the capability to do parameter estimation for a function. CoReJava is unique in that functional forms for regression analysis are expressed as first-class citizens, i.e., as Java programs, in which some parameters are not a priori known, but need to be learned from training sets provided as input. Typical applications of CoReJava include calibration of parameters of computational processes, described as OO programs. To implement regression learning, the CoReJava compiler (1) analyses the structure of the parameterized Java program that represent a functional form, (2) automatically generates a constraint optimization problem, in which constraint variables are the unknown parameters, and the objective function to be minimized is the sum of squares of errors w.r.t. the training set, and (3) solves the optimization problem using an external non-linear optimization solver. CoReJava then executes as a regular Java program, in which the initially unknown parameters are replaced with the found optimal values. CoReJava syntax and semantics are formally defined and exemplified using a simple supply chain example.

This paper proposes a framework for Composite Alternative Recommendation Development (CARD), which supports composite product and service definitions, top-k decision optimization, and dynamic preference learning. Composite services are characterized by a set of sub-services, which, in turn, can be composite or atomic. Each atomic and composite service is associated with metrics, such as cost, duration, and enjoyment ranking. The framework is based on the Composite Recommender Knowledge Base, which is composed of views, including Service Metric Views that specify services and their metrics; Recommendation Views that specify the ranking definition to balance optimality and diversity; parametric Transformers that specify how service metrics are defined in terms of metrics of its subservices; and learning sets from which the unknown parameters in the transformers are iteratively learned. Also introduced in the paper is the top-k selection criterion that, based on a vector of utility metrics, provides the balance between the optimality of individual metrics and the diversity of recommendations. To exemplify the framework, specific views are developed for a travel package recommender system.

This paper introduces an architectural style for enabling end-users to quickly design and deploy software systems in domains characterized by highly personalized and dynamic requirements. The style offers an intuitive metaphor based on boxes, pipes, and wires, but retains enough preciseness that systems can be automatically assembled and dynamically reconfigured based on uDesign descriptions. uDesign was primarily motivated and validated within monitoring and control applications for smart spaces, but we envision possible extensions to other domains. Our contribution differs from early attempts at end- user programming by dealing with higher level software architectural abstractions rather than programming, and by addressing run-time descriptions rather than code structures. The paper presents validation of uDesign along the following aspects: (a) expressiveness, by means of two case studies, one in health care, and one in home security, (b) soundness, by providing uDesign's formal semantics, and (c) implementability, by describing a mapping of uDesign to an existing software infrastructure: the Aura infrastructure.

The problem of information disclosure has attracted much interest from the research community in recent years. When disclosing information, the challenge is to provide as much information as possible (optimality) while guaranteeing a desired safety property for privacy (such as l-diversity). A typical disclosure algorithm uses a sequence of disclosure schemas to output generalizations in the nonincreasing order of data utility; the algorithm releases the first generalization that satisfies the safety property. In this paper, we assert that the desired safety property cannot always be guaranteed if an adversary has the knowledge of the underlying disclosure algorithm. We propose a model for the additional information disclosed by an algorithm based on the definition of deterministic disclosure function (DDF), and provide definitions of p-safe and p-optimal DDFs. We give an analysis for the complexity to compute a p-optimal DDF. We show that deciding whether a DDF is p-optimal is an NP-hard problem, and only under specific conditions, we can solve the problem in polynomial time with respect to the size of the set of all possible database instances and the length of the disclosure generalization sequence. We then consider the problem of microdata disclosure and the safety condition of l-diversity. We relax the notion of p-optimality to weak p-optimality, and develop a weak p-optimal algorithm which is polynomial in the size of the original table and the length of the generalization sequence.

We introduce regression databases (REDB) to formalize and automate probabilistic querying using sparse learning sets. The REDB data model involves observation data, learning set data, views definitions, and a regression model instance. The observation data is a collection of relational tuples over a set of attributes; the learning data set involves a subset of observation tuples, augmented with learned attributes, which are modeled as random variables; the views are expressed as linear combinations of observation and learned attributes; and the regression model involves functions that map observation tuples to probability distributions of the random variables, which are learned dynamically from the learning data set. The REDB query language extends relational algebra project-select queries with conditions on probabilities of first-order logical expressions, which in turn involve linear combinations of learned attributes and views, and arithmetic comparison operators. Such capability relies on the underlying regression model for the learned attributes. We show that REDB queries are computable by developing conceptual evaluation algorithms and by proving their correctness and termination

Investigates the problem of inference channels that occur when database constraints are combined with non-sensitive data to obtain sensitive information. We present an integrated security mechanism, called the Disclosure Monitor, which guarantees data confidentiality by extending the standard mandatory access control mechanism with a Disclosure Inference Engine. This generates all the information that can be disclosed to a user based on the user's past and present queries and the database and metadata constraints. The Disclosure Inference Engine operates in two modes: a data-dependent mode, when disclosure is established based on the actual data items, and a data-independent mode, when only queries are utilized to generate the disclosed information. The disclosure inference algorithms for both modes are characterized by the properties of soundness (i.e. everything that is generated by the algorithm is disclosed) and completeness (i.e. everything that can be disclosed is produced by the algorithm). The technical core of this paper concentrates on the development of sound and complete algorithms for both data-dependent and data-independent disclosures.

The purpose of good database logical design is to eliminate data redundancy and isertion and deletion anomalies. In order to achieve this objective for temporal databases, the notions of temporal types , which formalize time granularities, and temporal functional dependencies (TFDs) are intrduced. A temporal type is a monotonic mapping from ticks of time (represented by positive integers) to time sets (represented by subsets of reals) and is used to capture various standard and user-defined calendars. A TFD is a proper extension of the traditional functional dependency and takes the form X → μ Y, meaning that there is a unique value for Y during one tick of the temporal type μ for one particular X value. An axiomatization for TFDs is given. Because a finite set TFDs usually implies an infinite number of TFDs, we introduce the notion of and give an axiomatization for a finite closure to effectively capture a finite set of implied TFDs that are essential of the logical design. Temporal normalization procedures with respect to TFDs are given. Specifically, temporal Boyce-Codd normal form (TBCNF) that avoids all data redundancies due to TFDs, and temporal third normal form (T3NF) that allows dependency preservation, are defined. Both normal forms are proper extensions of their traditional counterparts, BCNF and 3NF. Decompositition algorithms are presented that give lossless TBCNF decompositions and lossless, dependency-preserving, T3NF decompositions.

The filtering method considered in this paper is based on approximation of a spatial object in d-dimensional space by the minimal convex polyhedron that encloses the object and whose facets are normal to preselected axes. These axes are not necessarily the standard coordinate axes and, furthermore, their number is not determined by the dimension of the space. We optimize filtering by selecting optimal such axes based on a pre-processing analysis of stored objects or a sample thereof. The number of axes selected represents a trade-off between access time and storage overhead, as more axes usually lead to better filtering but require more overhead to store the associated access structures. We address the problem of minimizing the number of axes required to achieve a predefined quality of filtering and the reverse problem of optimizing the quality of filtering when the number of axes is fixed. In both cases we also show how to find an optimal collection of axes. In order to sol...

We propose a novel data model and its language for querying object-oriented databases where objects may hold spatial, temporal or constraint data, conceptually represented by linear equality and inequality constraints. The proposed LyriC language is designed to provide a uniform and flexible framework for diverse application realms such as (1) constraint-based design in two-, three-, or higher-dimensional space, (2) large-scale optimization and analysis, based mostly on linear programming techniques, and (3) spatial and geographic databases. LyriC extends flat constraint query languages, especially those for linear constraint databases, to structurally complex objects. The extension is based on the object-oriented paradigm, where constraints are treated as first-class objects that are organized in classes. The query language is an extension of the language XSQL, and is built around the idea of extended path expressions. Path expressions in a query traverse nested structures in one sweep. Constraints are used in a query to filter stored constraints and to create new constraint objects.

Article Free Access Share on Inference of inequality constraints in logic programs (extended abstracts) Authors: Alexander Brodsky Hebrew University Hebrew UniversityView Profile , Yehoshua Sagiv Hebrew University and Stanford University Hebrew University and Stanford UniversityView Profile Authors Info & Claims PODS '91: Proceedings of the tenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systemsApril 1991 Pages 227–240https://doi.org/10.1145/113413.113434Online:01 April 1991Publication History 27citation263DownloadsMetricsTotal Citations27Total Downloads263Last 12 Months5Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Datalog (i.e., function-free logic) programs with monotonicity constraints on extensional predicates are considered. A monotonicity constraint states that one argument of a predicate is always less than another argument, according to some partial order. Relations of an extensional database are required to satisfy the monotonicity constraints imposed on their predicates. More specifically, a partial order is defined on the domain (i.e., set of constants) of the database, and every tuple of each relation satisfies the monotonicity constraints imposed on its predicate. An algorithm is given for inferring all monotonicity constraints that hold in relations of the intensional database from monotonicity constraints that hold in the extensional database. A complete inference algorithm is also given for disjunctions of monotonicity and equality constraints. It is shown that the inference of monotonicity constraints in programs is a complete problem for exponential time. For linear programs, this problem is complete for polynomial space.