Handling non-answers is desirable in information retrieval systems. Current e-commerce websites usually try to suppress the somewhat dreaded message that no results have been found. Possible solutions include, for example, augmenting the data with synonyms and common misspellings based on query logs. Nonetheless, this is only achievable if we can know the cause of the non-answers. Under the hood, most e-commerce data sits in some structured format. Debugging non-answers in the underlying KWS-S systems is therefore not trivial — non-answers in a KWS-S system could be a problem of the data (e.g., absence of some keywords), the schema (e.g., missing key-foreign-key joins), or due to empty join results from one of possibly several joins in the generated SQL queries. So far, we are unaware of any previous work that explores how to enable developers to debug non-answers in a KWS-S system. In this paper, we take a first step towards this direction by proposing a KWS-S system that can expose non-answers to the developers. Our system presents the developers with the maximal nonempty sub-queries that represent the frontier cause of the non-answers. We outline the challenges in building such a system and propose a lattice structure for efficient exploration of the non-answer query space. We also evaluate our proposed mechanisms over a real world dataset to demonstrate their feasibility.

Keyword search (KWS) over relational data, where the answers are multiple tuples connected via joins, has received significant attention in the past decade. Numerous solutions have been proposed and many prototypes have been developed. Building on this rapid progress and on growing user needs, recently several RDBMS and Web companies as well as academic research groups have started to examine how to build industrial-strength keywords search systems. This task clearly requires addressing many issues, including robustness, accuracy, reliability, and privacy, among others. A major emerging issue, however, appears to be performance related: current KWS systems have unpredictable run time. In particular, for certain queries it takes too long to produce answers, and for others the system may even fail to return (e.g., after exhausting memory). In this paper we begin by examining the above problem and arguing that it is a fundamental problem unlikely to be solved in the near future by software and hardware advances. Next, we argue that in an industrial-strength setting, to ensure real-time interaction and facilitate user adoption, KWS systems should produce answers under an absolute time limit and then provide users with a description of what could be done next, should he or she choose to continue. Next, we show how to realize these requirements for DISCOVER, an exemplar of a recent KWS solution approach. Our basic idea is to produce answers as in today's KWS systems up to the time limit, then show users these answers as well as query forms that characterize the unexplored portion of the answer space. Finally, we present some preliminary experiments over real-world data to demonstrate the feasibility of the proposed solution approach.

Keyword search (KWS) over relational databases has recently received significant attention. Many solutions and many prototypes have been developed. This task requires addressing many issues, including robustness, accuracy, reliability, and privacy. An emerging issue, however, appears to be performance related: current KWS systems have unpredictable running times. In particular, for certain queries it takes too long to produce answers, and for others the system may even fail to return (e.g., after exhausting memory). In this paper we argue that as today's users have been "spoiled" by the performance of Internet search engines, KWS systems should return whatever answers they can produce quickly and then provide users with options for exploring any portion of the answer space not covered by these answers. Our basic idea is to produce answers that can be generated quickly as in today's KWS systems, then to show users query forms that characterize the unexplored portion of the answer space. Combining KWS systems with forms allows us to bypass the performance problems inherent to KWS without compromising query coverage. We provide a proof of concept for this proposed approach, and discuss the challenges encountered in building this hybrid system. Finally, we present experiments over real-world datasets to demonstrate the feasibility of the proposed solution.

A common criticism of database systems is that they are hard to query for users uncomfortable with a formal query language. To address this problem, form-based interfaces and keyword search have been proposed; while both have benefits, both also have limitations. In this paper, we investigate combining the two with the hopes of creating an approach that provides the best of both. Specifically, we propose to take as input a target database and then generate and index a set of query forms offline. At query time, a user with a question to be answered issues standard keyword search queries; but instead of returning tuples, the system returns forms relevant to the question. The user may then build a structured query with one of these forms and submit it back to the system for evaluation. In this paper, we address challenges that arise in form generation, keyword search over forms, and ranking and displaying these forms. We explore techniques to tackle these challenges, and present experimental results suggesting that the approach of combining keyword search and form-based interfaces is promising.

DBPubs is a system for effectively analyzing and exploring the content of database publications by combining keyword search with OLAP-style aggregations, navigation, and reporting. DBPubs starts with keyword search over the content of publications. The publications' metadata such as title, authors, venues, year, and so on, provide traditional OLAP static dimensions, which are combined with dynamic dimensions discovered from the content of the publications in the search result, such as frequent phrases, relevant phrases, and topics. We compute publication ranks based on the link structure between documents, i.e., citations, and aggregate them to find seminal papers, discover trends, and rank authors. We deploy an OLAP tool for multidimensional content exploration through traditional OLAP rollup-drilldown operations on the static and dynamic dimensions, solutions for multi-cube analysis, dynamic navigation of the content, and highlighting of interesting dices of the multidimensional content dataspace.

Content Management Systems (CMS) store enterprise data such as insurance claims, insurance policies, legal documents, patent applications, or archival data like in the case of digital libraries. Search over content allows for information retrieval, but does not provide users with great insight into the data. A more analytical view is needed through analysis, aggregations, groupings, trends, pivot tables or charts, and so on. Multidimensional Content eXploration (MCX) is about effectively analyzing and exploring large amounts of content by combining keyword search with OLAP-style aggregation, navigation, and reporting. We focus on unstructured data or generally speaking documents or content with limited metadata, as it is typically encountered in CMS. We formally present how CMS content and metadata should be organized in a well-defined multidimensional structure, so that sophisticated queries can be expressed and evaluated. The CMS metadata provide traditional OLAP static dimensions that are combined with dynamic dimensions discovered from the analyzed keyword search result, as well as measures for document scores based on the link structure between the documents. In addition, we provide means for multidimensional content exploration through traditional OLAP rollupdrilldown operations on the static and dynamic dimensions, solutions for multi-cube analysis and dynamic navigation of the content. We present our prototype, called DBPubs, which stores research publications as documents that can be searched and -most importantly-- analyzed, and explored. Finally, we present experimental results of the efficiency and effectiveness of our approach.