Data integration still remains largely rule-driven and lacks universal automation. In this work, we propose a general approach to modeling and integrating entities from structured data, such as relational databases, as well as unstructured sources, such as free text from news articles. Our approach is designed to explicitly model and leverage relations between entities, thereby using all available information and preserving as much context as possible. This is achieved by combining siamese and graph neural networks to propagate information between connected entities. We evaluate our method on the task of integrating data about business entities and demonstrate that it outperforms standard rule-based systems, as well as other deep-learning approaches that do not leverage any inductive bias towards the use of graph-based representations.

While natural language systems continue improving, they are still imperfect. If a user has a better understanding of how a system works, they may be able to better accomplish their goals even in imperfect systems. We explored whether explanations can support effective authoring of natural language utterances and how those explanations impact users’ mental models in the context of a natural language system that generates small programs. Through an online study (n=252), we compared two main types of explanations: 1) system-focused, which provide information about how the system processes utterances and matches terms to a knowledge base, and 2) social, which provide information about how other users have successfully interacted with the system. Our results indicate that providing social suggestions of terms to add to an utterance helped users to repair and generate correct flows more than system-focused explanations or social recommendations of words to modify. We also found that participants commonly understood some mechanisms of the natural language system, such as the matching of terms to a knowledge base, but they often lacked other critical knowledge, such as how the system handled structuring and ordering. Based on these findings, we make design recommendations for supporting interactions with and understanding of natural language systems.

Web applications and services are increasingly important in a distributed internet filled with diverse cloud services and applications, each of which enable the completion of narrowly defined tasks. Given the explosion in the scale and diversity of such services, their composition and integration for achieving complex user goals remains a challenging task for end-users and requires a lot of development effort when specified by hand. We present a demonstration of the Goal Oriented Flow Assistant (GOFA) system, which provides a natural language solution to generate workflows for application integration. Our tool is built on a three-step pipeline: it first uses Abstract Meaning Representation (AMR) to parse utterances; it then uses a knowledge graph to validate candidates; and finally uses an AI planner to compose the candidate flow. We provide a video demonstration of the deployed system as part of our submission.

Data integration has been studied extensively for decades and approached from different angles. However, this domain still remains largely rule-driven and lacks universal automation. Recent developments in machine learning and in particular deep learning have opened the way to more general and efficient solutions to data-integration tasks. In this paper, we demonstrate an approach that allows modeling and integrating entities by leveraging their relations and contextual information. This is achieved by combining siamese and graph neural networks to effectively propagate information between connected entities and support high scalability. We evaluated our approach on the task of integrating data about business entities, demonstrating that it outperforms both traditional rule-based systems and other deep learning approaches.

Record linkage is an essential part of nearly all real-world systems that consume structured and unstructured data coming from different sources. Typically no common key is available for connecting records. Massive data integration processes often have to be completed before any data analytics and further processing can be performed. In this work we focus on company entity matching, where company name, location and industry are taken into account. Our contribution is a highly scalable, enterprise-grade end-to-end system that uses rule-based linkage algorithms in combination with a machine learning approach to account for short company names. Linkage time is greatly reduced by an efficient decomposition of the search space using MinHash. Based on real-world ground truth datasets, we show that our approach reaches a recall of 91% compared to 73% for baseline approaches, while scaling linearly with the number of nodes used in the system.

Wireless sensor networks are used to monitor a given environment, such as indoor heating conditions or the micro-climate of glaciers. They offer a low-cost solution that provides a high data density. Usually the user of such a sensor network has a good idea of how, knowing the environment, the sensed values should behave. This idea can be expressed as a data model. Such models can be used to detect anomalies, compress data, or combine data from many inexpensive sensors to increase the quality of the measurements. This paper presents a framework to process arbitrary sensor-network data models. The framework can then be used to distribute the model processing into the wireless sensor network. Quality of information criteria are used to determine the performance of the models. A prototype of the framework is presented together with a comparison of two existing stochastic data model approaches for wireless sensor networks.

Cheap programmable sensor devices are becoming commercially available. They offer the possibility of transforming existing enterprise applications and enabling entirely new ones. The merging of sensor networks into the enterprise network poses some distinct problems. In particular, information from theses devices must be obtained in a way which minimizes their energy use and must be aggregated and filtered before being sent to the application server to prevent it from being overwhelmed. We describe a range of complementary techniques for integrating sensors into an enterprise network. These comprise new architectural entities within the enterprise network — edge server — new means of sharing information within the enterprise network — messaging binning — and new protocols for extracting information from the sensor network — Messo.

Edge servers are used to connect sensor networks to the enterprise network. Using asynchronous messaging for communication between components is attractive as it lessens the degree of coupling within the system, thereby increasing the system robustness. In order to be scalable, the messaging system itself must be distributed. Existing work in the literature describes how to build distributed messaging systems which scale to networks the size of the Internet using message routing. Here we explain why we find these solutions inappropriate for communication within the edge server architecture. We describe an alternative means of achieving scalable distributed messaging that we call message binning. We motivate the feasibility of our architecture through a description of its implementation and use in our edge server system.

Floating car data (FCD), i.e. traffic and in-car data collected and transmitted by moving cars, is an emerging telematics application providing a range of networked services to road users. FCD is an example of a large sensor network, whose complexity increases with the number of session participants. Our work examines how deploying application-specific functions in the network can help in scaling such systems. We have chosen to configure these functions on a dedicated device that is commonly called a middlebox. These middleboxes use programmable network processors in order to attain the required processing and forwarding speeds, while communicating with each other and a back-end server using standard middleware components. We present our experience in using such a programmable middlebox in the scaling of a large telematics service, explaining how the middlebox fits into the end-to-end application.

In a hierarchical network, groups of nodes are represented by logical nodes for the purposes of simplifying routing. Each group has a set of ingress-egress nodes, and routing information is conveyed to the outside world in the form of a transition matrix that gives the cost of traversing the network between each ingress-egress node pair. In this paper, we present a minimal logical node representation that has sufficient descriptive power to enable path selection in support of connection admission control for services that have both path (restrictive) and link (additive) constraints. For example, the representation can be used to find a path that maximizes bandwidth subject to a delay constraint or minimizes delay subject to a bandwidth constraint. We present our solution in the form of a matrix whose elements are vectors, each of which specifies the efficient frontier of the solution space, and we specify an efficient procedure for constructing the efficient frontier. We present the least upper bound on the number of elements that must be present in the efficient frontier. We provide numerical examples that illustrate construction of the efficient frontier.