Janet L. Wiener

Bio: Janet L. Wiener is an academic researcher from Stanford University. The author has contributed to research in topics: Data warehouse & Dimensional modeling. The author has an hindex of 21, co-authored 29 publications receiving 6128 citations. Previous affiliations of Janet L. Wiener include Hewlett-Packard & University of Wisconsin-Madison.

Graph structure in the Web

Abstract: The study of the web as a graph is not only fascinating in its own right, but also yields valuable insight into web algorithms for crawling, searching and community discovery, and the sociological phenomena which characterize its evolution. We report on experiments on local and global properties of the web graph using two Altavista crawls each with over 200 million pages and 1.5 billion links. Our study indicates that the macroscopic structure of the web is considerably more intricate than suggested by earlier experiments on a smaller scale.

The Lorel Query Language for Semistructured Data

Abstract: language, designed for querying semistructured data. Semistructured data is becoming more and more prevalent, e.g., in structured documents such as HTML and when performing simple integration of data from multiple sources. Traditional data models and query languages are inappropriate, since semistructured data often is irregular: some data is missing, similar concepts are represented using different types, heterogeneous sets are present, or object structure is not fully known. Lorel is a user-friendly language in the SQL/OQL style for querying such data effectively. For wide applicability, the simple object model underlying Lorel can be viewed as an extension of the ODMG data model and the Lorel language as an extension of OQL. The main novelties of the Lorel language are: (i) the extensive use of coercion to relieve the user from the strict typing of OQL, which is inappropriate for semistructured data; and (ii) powerful path expressions, which permit a flexible form of declarative navigational access and are particularly suitable when the details of the structure are not known to the user. Lorel also includes a declarative update language. Lorel is implemented as the query language of the Lore prototype database management system at Stanford. Information about Lore can be found at http://www-db.stanford.edu/lore. In addition to presenting the Lorel language in full, this paper briefly describes the Lore system and query processor. We also briefly discuss a second implementation of Lorel on top of a conventional object-oriented database management system, the O2 system.

Tracing the lineage of view data in a warehousing environment

Abstract: We consider the view data lineageproblem in a warehousing environment: For a given data item in a materialized warehouse view, we want to identify the set of source data items that produced the view item. We formally define the lineage problem, develop lineage tracing algorithms for relational views with aggregation, and propose mechanisms for performing consistent lineage tracing in a multisource data warehousing environment. Our result can form the basis of a tool that allows analysts to browse warehouse data, select view tuples of interest, and then “drill-through” to examine the exact source tuples that produced the view tuples of interest.

Breadth-first crawling yields high-quality pages

Abstract: This paper examines the average page quality over time of pages downloaded during a web crawl of 328 million unique pages. We use the connectivity-based metric PageRank to measure the quality of a page. We show that traversing the web graph in breadth-first search order is a good crawling strategy, as it tends to discover high-quality pages early on in the crawl.

Representative objects: concise representations of semistructured, hierarchical data

Abstract: Introduces the concept of representative objects, which uncover the inherent schema(s) in semi-structured, hierarchical data sources and provide a concise description of the structure of the data. Semi-structured data, unlike data stored in typical relational or object-oriented databases, does not have a fixed schema that is known in advance and stored separately from the data. With the rapid growth of the World Wide Web, semi-structured hierarchical data sources are becoming widely available to the casual user. The lack of external schema information currently makes browsing and querying these data sources inefficient at best, and impossible at worst. We show how representative objects make schema discovery efficient and facilitate the generation of meaningful queries over the data.

Statistical mechanics of complex networks

Abstract: The emergence of order in natural systems is a constant source of inspiration for both physical and biological sciences. While the spatial order characterizing for example the crystals has been the basis of many advances in contemporary physics, most complex systems in nature do not offer such high degree of order. Many of these systems form complex networks whose nodes are the elements of the system and edges represent the interactions between them. Traditionally complex networks have been described by the random graph theory founded in 1959 by Paul Erdohs and Alfred Renyi. One of the defining features of random graphs is that they are statistically homogeneous, and their degree distribution (characterizing the spread in the number of edges starting from a node) is a Poisson distribution. In contrast, recent empirical studies, including the work of our group, indicate that the topology of real networks is much richer than that of random graphs. In particular, the degree distribution of real networks is a power-law, indicating a heterogeneous topology in which the majority of the nodes have a small degree, but there is a significant fraction of highly connected nodes that play an important role in the connectivity of the network. The scale-free topology of real networks has very important consequences on their functioning. For example, we have discovered that scale-free networks are extremely resilient to the random disruption of their nodes. On the other hand, the selective removal of the nodes with highest degree induces a rapid breakdown of the network to isolated subparts that cannot communicate with each other. The non-trivial scaling of the degree distribution of real networks is also an indication of their assembly and evolution. Indeed, our modeling studies have shown us that there are general principles governing the evolution of networks. Most networks start from a small seed and grow by the addition of new nodes which attach to the nodes already in the system. This process obeys preferential attachment: the new nodes are more likely to connect to nodes with already high degree. We have proposed a simple model based on these two principles wich was able to reproduce the power-law degree distribution of real networks. Perhaps even more importantly, this model paved the way to a new paradigm of network modeling, trying to capture the evolution of networks, not just their static topology.

The Structure and Function of Complex Networks

Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

Community structure in social and biological networks

Abstract: A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known—a collaboration network and a food web—and find that it detects significant and informative community divisions in both cases.

Finding and evaluating community structure in networks.

Abstract: We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.