Two Case Studies of Subsystem Design for General-Purpose CSCW Software Architectures

This paper discusses subsystem design guidelines for the software architecture of general-purpose computer supported cooperative work systems, i.e., systems that are designed to be applicable in various application areas requiring explicit collaboration support. In our opinion, guidelines for subsystem level design are rarely given most guidelines currently given apply to the programming language level. We extract guidelines from a case study of the redesign and extension of an advanced commercial workflow management system and place them into the context of existing software engineering research. The guidelines are then validated against the design decisions made in the construction of a widely used web-based groupware system. Our approach is based on the well-known distinction between essential (logical) and physical architectures. We show how essential architecture design can be based on a direct mapping of abstract functional concepts as found in general-purpose systems to modules in the essential architecture. The essential architecture is next mapped to a physical architecture by applying software clustering and replication to achieve the required distribution and performance characteristics

Protocols for Integrity Constraint Checking in Federated Databases

A federated database is comprised of multiple interconnected database systems that primarily operate independently but cooperate to a certain extent. Global integrity constraints can be very useful in federated databases, but the lack of global queries, global transaction mechanisms, and global concurrency control renders traditional constraint management techniques inapplicable. This paper presents a threefold contribution to integrity constraint checking in federated databases: (1) The problem of constraint checking in a federated database environment is clearly formulated. (2) A family of protocols for constraint checking is presented. (3) The differences across protocols in the family are analyzed with respect to system requirements, properties guaranteed by the protocols, and processing and communication costs. Thus, our work yields a suite of options from which a protocol can be chosen to suit the system capabilities and integrity requirements of a particular federated database environment

Physical Representation-based Predicate Optimization for a Visual Analytics Database

Querying the content of images, video, and other non-textual data sources requires expensive content extraction methods. Modern extraction techniques are based on deep convolutional neural networks (CNNs) and can classify objects within images with astounding accuracy. Unfortunately, these methods are slow: processing a single image can take about 10 milliseconds on modern GPU-based hardware. As massive video libraries become ubiquitous, running a content-based query over millions of video frames is prohibitive. One promising approach to reduce the runtime cost of queries of visual content is to use a hierarchical model, such as a cascade, where simple cases are handled by an inexpensive classifier. Prior work has sought to design cascades that optimize the computational cost of inference by, for example, using smaller CNNs. However, we observe that there are critical factors besides the inference time that dramatically impact the overall query time. Notably, by treating the physical representation of the input image as part of our query optimization---that is, by including image transforms, such as resolution scaling or color-depth reduction, within the cascade---we can optimize data handling costs and enable drastically more efficient classifier cascades. In this paper, we propose Tahoma, which generates and evaluates many potential classifier cascades that jointly optimize the CNN architecture and input data representation. Our experiments on a subset of ImageNet show that Tahoma's input transformations speed up cascades by up to 35 times. We also find up to a 98x speedup over the ResNet50 classifier with no loss in accuracy, and a 280x speedup if some accuracy is sacrificed.Comment: Camera-ready version of the paper submitted to ICDE 2019, In Proceedings of the 35th IEEE International Conference on Data Engineering (ICDE 2019

A new approach for discovering business process models from event logs.

Process mining is the automated acquisition of process models from the event logs of information systems. Although process mining has many useful applications, not all inherent difficulties have been sufficiently solved. A first difficulty is that process mining is often limited to a setting of non-supervised learnings since negative information is often not available. Moreover, state transitions in processes are often dependent on the traversed path, which limits the appropriateness of search techniques based on local information in the event log. Another difficulty is that case data and resource properties that can also influence state transitions are time-varying properties, such that they cannot be considered ascross-sectional.This article investigates the use of first-order, ILP classification learners for process mining and describes techniques for dealing with each of the above mentioned difficulties. To make process mining a supervised learning task, we propose to include negative events in the event log. When event logs contain no negative information, a technique is described to add artificial negative examples to a process log. To capture history-dependent behavior the article proposes to take advantage of the multi-relational nature of ILP classification learners. Multi-relational process mining allows to search for patterns among multiple event rows in the event log, effectively basing its search on global information. To deal with time-varying case data and resource properties, a closed-world version of the Event Calculus has to be added as background knowledge, transforming the event log effectively in a temporal database. First experiments on synthetic event logs show that first-order classification learners are capable of predicting the behavior with high accuracy, even under conditions of noise.Credit; Credit scoring; Models; Model; Applications; Performance; Space; Decision; Yield; Real life; Risk; Evaluation; Rules; Neural networks; Networks; Classification; Research; Business; Processes; Event; Information; Information systems; Systems; Learning; Data; Behavior; Patterns; IT; Event calculus; Knowledge; Database; Noise;

Modeling Relational Data via Latent Factor Blockmodel

In this paper we address the problem of modeling relational data, which appear in many applications such as social network analysis, recommender systems and bioinformatics. Previous studies either consider latent feature based models but disregarding local structure in the network, or focus exclusively on capturing local structure of objects based on latent blockmodels without coupling with latent characteristics of objects. To combine the benefits of the previous work, we propose a novel model that can simultaneously incorporate the effect of latent features and covariates if any, as well as the effect of latent structure that may exist in the data. To achieve this, we model the relation graph as a function of both latent feature factors and latent cluster memberships of objects to collectively discover globally predictive intrinsic properties of objects and capture latent block structure in the network to improve prediction performance. We also develop an optimization transfer algorithm based on the generalized EM-style strategy to learn the latent factors. We prove the efficacy of our proposed model through the link prediction task and cluster analysis task, and extensive experiments on the synthetic data and several real world datasets suggest that our proposed LFBM model outperforms the other state of the art approaches in the evaluated tasks.Comment: 10 pages, 12 figure

Nonparametric Bayes dynamic modeling of relational data

Symmetric binary matrices representing relations among entities are commonly collected in many areas. Our focus is on dynamically evolving binary relational matrices, with interest being in inference on the relationship structure and prediction. We propose a nonparametric Bayesian dynamic model, which reduces dimensionality in characterizing the binary matrix through a lower-dimensional latent space representation, with the latent coordinates evolving in continuous time via Gaussian processes. By using a logistic mapping function from the probability matrix space to the latent relational space, we obtain a flexible and computational tractable formulation. Employing P\`olya-Gamma data augmentation, an efficient Gibbs sampler is developed for posterior computation, with the dimension of the latent space automatically inferred. We provide some theoretical results on flexibility of the model, and illustrate performance via simulation experiments. We also consider an application to co-movements in world financial markets

Human-Level Performance on Word Analogy Questions by Latent Relational Analysis

This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, machine translation, and information retrieval. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason/stone is analogous to the pair carpenter/wood; the relations between mason and stone are highly similar to the relations between carpenter and wood. Past work on semantic similarity measures has mainly been concerned with attributional similarity. For instance, Latent Semantic Analysis (LSA) can measure the degree of similarity between two words, but not between two relations. Recently the Vector Space Model (VSM) of information retrieval has been adapted to the task of measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus (they are not predefined), (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data (it is also used this way in LSA), and (3) automatically generated synonyms are used to explore reformulations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying noun-modifier relations, LRA achieves similar gains over the VSM, while using a smaller corpus