The representation learning on textual graph is to generate low-dimensional embeddings for the nodes based on the individual textual features and the neighbourhood information. Recent breakthroughs on pretrained language models and graph neural networks push forward the development of corresponding techniques. The existing works mainly rely on the cascaded model architecture: the textual features of nodes are independently encoded by language models at first; the textual embeddings are aggregated by graph neural networks afterwards. However, the above architecture is limited due to the independent modeling of textual features. In this work, we propose GraphFormers, where layerwise GNN components are nested alongside the transformer blocks of language models. With the proposed architecture, the text encoding and the graph aggregation are fused into an iterative workflow, making each node's semantic accurately comprehended from the global perspective. In addition, a progressive learning strategy is introduced, where the model is successively trained on manipulated data and original data to reinforce its capability of integrating information on graph. Extensive evaluations are conducted on three large-scale benchmark datasets, where GraphFormers outperform the SOTA baselines with comparable running efficiency. The source code is released at https://github.com/microsoft/GraphFormers .

Many web queries seek information about named entities (such as products or people). Web search engines federate such entity-oriented queries to relevant structured databases; the results of those searches are then returned to the user along with web search results. Current federated approaches have two limitations: (i) they often fail to return important results for a broad class of such entity-oriented queries and (ii) the information they return per entity is often inadequate. In this paper, we present the Query Portals system that addresses these limitations. The Query Portals system dynamically generates a portal for an entity-oriented query. It first provides an overview of the relevant entities and further allows users to drill down to gather additional information on these entities. Our architecture uses a judicious combination of pre-processing and query time techniques so that the query portal can be generated efficiently.

Web search engines often federate many user queries to relevant structured databases. For example, a product related query might be federated to a product database containing their descriptions and specifications. The relevant structured data items are then returned to the user along with web search results. However, each structured database is searched in isolation. Hence, the search often produces empty or incomplete results as the database may not contain the required information to answer the query. In this paper, we propose a novel integrated search architecture. We establish and exploit the relationships between web search results and the items in structured databases to identify the relevant structured data items for a much wider range of queries.Our architecture leverages existing search engine components to implement this functionality at very low overhead. We demonstrate the quality and efficiency of our techniques through an extensive experimental study.

Supporting entity extraction from large document collections is important for enabling a variety of important data analysis tasks. In this paper, we introduce the "ad-hoc" entity extraction task where entities of interest are constrained to be from a list of entities that is specific to the task. In such scenarios, traditional entity extraction techniques that process all the documents for each ad-hoc entity extraction task can be significantly expensive. In this paper, we propose an efficient approach that leverages the inverted index on the documents to identify the subset of documents relevant to the task and processes only those documents. We demonstrate the efficiency of our techniques on real datasets.

The area of automatic selection of physical database design to optimize the performance of a relational database system based on a workload of SQL queries and updates has gained prominence in recent years. Major database vendors have released automated physical database design tools with the goal of reducing the total cost of ownership. An important assumption underlying these tools is that the workload is a set of SQL statements. In this paper, we show that being able to treat the workload as a sequence, i.e., exploiting the ordering of statements can significantly broaden the usage of such tools. We present scenarios where exploiting sequence information in the workload is crucial for performance tuning. We also propose techniques for addressing the technical challenges arising from treating the workload as a sequence. We evaluate the effectiveness of our techniques through experiments on Microsoft SQL Server.

Database Tuning Advisor (DTA) is a physical database design tool that is part of Microsoft's SQL Server 2005 relational database management system. Previously known as "Index Tuning Wizard" in SQL Server 7.0 and SQL Server 2000, DTA adds new functionality that is not available in other contemporary physical design tuning tools. Novel aspects of DTA that will be demonstrated include: (a) Ability to take into account both performance and manageability requirements of DBAs (b) Fully integrated recommendations for indexes, materialized views and horizontal partitioning (c) Transparently leverage a test server to offload tuning load from production server and (d) Easy programmability and scriptability.

In addition to indexes and materialized views, horizontal and vertical partitioning are important aspects of physical design in a relational database system that significantly impact performance. Horizontal partitioning also provides manageability; database administrators often require indexes and their underlying tables partitioned identically so as to make common operations such as backup/restore easier. While partitioning is important, incorporating partitioning makes the problem of automating physical design much harder since: (a) The choices of partitioning can strongly interact with choices of indexes and materialized views. (b) A large new space of physical design alternatives must be considered. (c) Manageability requirements impose a new constraint on the problem. In this paper, we present novel techniques for designing a scalable solution to this integrated physical design problem that takes both performance and manageability into account. We have implemented our techniques and evaluated it on Microsoft SQL Server. Our experiments highlight: (a) the importance of taking an integrated approach to automated physical design and (b) the scalability of our techniques. 1.

Internet search engines have popularized the keyword-based search paradigm. While traditional database management systems offer powerful query languages, they do not allow keyword-based search. In this paper, we discuss DBXplorer, a system that enables keyword-based searches in relational databases. DBXplorer has been implemented using a commercial relational database and Web server and allows users to interact via a browser front-end. We outline the challenges and discuss the implementation of our system, including results of extensive experimental evaluation.

No abstract available.

No abstract available.