Panagiotis Liakos

Pushing the Envelope in Graph Compression

We improve the state-of-the-art method for the compression of web and other similar graphs by introducing an elegant technique which further exploits the clustering properties observed in these graphs. The analysis and experimental evaluation of our method shows that it outperforms the currently best method of Boldi et al. by achieving a better compression ratio and retrieval time. Our method exhibits vast improvements on certain families of graphs, such as social networks, by taking advantage of their compressibility characteristics, and ensures that the compression ratio will not worsen for any graph, since it easily falls back to the state-of-the-art method.

A Distributed Infrastructure for Earth-Science Big Data Retrieval

Earth-Science data are composite, multi-dimensional and of significant size, and as such, continue to pose a number of ongoing problems regarding their management. With new and diverse information sources emerging as well as rates of generated data continuously increasing, a persistent challenge becomes more pressing: To make the information existing in multiple heterogeneous resources readily available. The widespread use of the XML data-exchange format has enabled the rapid accumulation of semi-structured metadata for Earth-Science data. In this paper, we exploit this popular use of XML and present the means for querying metadata emanating from multiple sources in a succinct and effective way. Thereby, we release the user from the very tedious and time consuming task of examining individual XML descriptions one by one. Our approach, termed Meta-Array Data Search (MAD Search), brings together diverse data sources while enhancing the user-friendliness of the underlying information sources. We gather metad...

On the Effect of Locality in Compressing Social Networks

We improve the state-of-the-art method for graph compression by exploiting the locality of reference observed in social network graphs. We take advantage of certain dense parts of those graphs, which enable us to further reduce the overall space requirements. The analysis and experimental evaluation of our method confirms our observations, as our results present improvements over a wide range of social network graphs.

Focused crawling for the hidden web

A constantly growing amount of high-quality information resides in databases and is guarded behind forms that users fill out and submit. The Hidden Web comprises all these information sources that conventional web crawlers are incapable of discovering. In order to excavate and make available meaningful data from the Hidden Web, previous work has focused on developing query generation techniques that aim at downloading all the content of a given Hidden Web site with the minimum cost. However, there are circumstances where only a specific part of such a site might be of interest. For example, a politics portal should not have to waste bandwidth or processing power to retrieve sports articles just because they are residing in databases also containing documents relevant to politics. In cases like this one, we need to make the best use of our resources in downloading only the portion of the Hidden Web site that we are interested in. We investigate how we can build a focused Hidden Web crawler that can autonomously extract topic-specific pages from the Hidden Web by searching only the subset that is related to the corresponding area. In this regard, we present an approach that progresses iteratively and analyzes the returned results in order to extract terms that capture the essence of the topic we are interested in. We propose a number of different crawling policies and we experimentally evaluate them with data from four popular sites. Our approach is able to download most of the content in search in all cases, using a significantly smaller number of queries compared to existing approaches.

An Interactive Freight-Pooling Service for Efficient Last-Mile Delivery

The existing practices of the urban section of freight transport chain result in traffic congestion, air pollution and resources being wasted. We focus on the final stage of freight distribution and propose an interactive freight-pooling service, in an effort to reduce the undesirable effects and the cost of freight transport in urban areas. Our service empowers city and state authorities to orchestrate the distribution network through interactive interfaces. We break the problem into three distinct phases that collectively helps us set constraints related to the quality of service and find inexpensive routes. In this regard, our proposed freight-pooling approach becomes an attractive option for efficient distribution, that guarantees cost minimization without sacrificing the level of quality.

Scalable link community detection: A local dispersion-aware approach

Real-life systems involving interacting objects are typically modeled as graphs and can often grow very large in size. Revealing the community structure of such systems is crucial in helping us better understand their complex nature. However, the ever-increasing size of real-world graphs, and our evolving perception of what a community is, make the task of community detection very challenging. One such challenge, is the discovery of the possibly overlapping communities of a given node in a billion-node graph. This problem is very common in modern large social networks like Facebook and Linkedln. In this paper, we propose a scalable local community detection approach to efficiently unfold the communities of individual target nodes in a given network. Our goal is to reveal the groupings formed around nodes (e.g., users) by leveraging the relations of the different contexts the nodes participate in. Our algorithm, termed Local Dispersion-aware Link Communities or LDLC, measures the similarity of pairs of links in the graph as well as the extent of their participation in multiple contexts. Then, it determines the ordering that we should group the links in order to form communities. Our approach is not affected by constraints existent in previous techniques (e.g., the need for several seed nodes or the need to collapse multiple overlapping communities to one). Our experimental evaluation using ground-truth communities for a wide range of large real-world networks show that LDLC significantly outperforms state-of-the-art methods on both accuracy and efficiency.

Memory-Optimized Distributed Graph Processing through Novel Compression Techniques

A multitude of contemporary applications now involve graph data whose size continuously grows and this trend shows no signs of subsiding. This has caused the emergence of many distributed graph processing systems including Pregel and Apache Giraph. However, the unprecedented scale now reached by real-world graphs hardens the task of graph processing even in distributed environments and the current memory usage patterns rapidly become a primary concern for such contemporary graph processing systems. We seek to address this challenge by exploiting empirically-observed properties demonstrated by graphs that are generated by human activity. In this paper, we propose three space-efficient adjacency list representations that can be applied to any distributed graph processing system. Our suggested compact representations reduce respective memory requirements for accommodating the graph elements up to 5 times if compared with state-of-the-art methods. At the same time, our memory-optimized methods retain the efficiency of uncompressed structures and enable the execution of algorithms for large scale graphs in settings where contemporary alternative structures fail due to memory errors.

Topic-Sensitive hidden-web crawling

A constantly growing amount of high-quality information is stored in pages coming from the Hidden Web. Such pages are accessible only through a query interface that a Hidden-Web site provides and may span a variety of topics. In order to provide centralized access to the Hidden Web, previous works have focused on query generation techniques that aim at downloading all content of a given Hidden Web site with the minimum cost. In certain settings however, we are interested in downloading only a specific part of such a site. For example, in a news database, a user may be interested in retrieving only sports articles but no politics. In this case, we need to make the best use of our resources in downloading only the portion of the Hidden Web site that we are interested in. In this paper, we study how we can build a topically-focused Hidden Web crawler that can autonomously extract topic-specific pages from the Hidden Web by searching only the subset that is related to the corresponding category. To this end, we present query generation techniques that take into account the topic that we are interested in. We propose a number of different crawling policies and we experimentally evaluate them with data from two popular sites.

xWCPS: Bridging the Gap Between Array and Semi-structured Data

The ever growing amount of information collected by scientific instruments and the presence of descriptive metadata accompanying them calls for a unified way of querying over array and semi-structured data. We present xWCPS, a novel query language that bridges the path between these two different worlds, enhancing the expressiveness and user-friendliness of previous approaches.

Realizing Memory-Optimized Distributed Graph Processing

A multitude of contemporary applications heavily involve graph data whose size appears to be ever–increasing. This trend shows no signs of subsiding and has caused the emergence of a number of distributed graph processing systems including Pregel, Apache Giraph, and GraphX . However, the unprecedented scale now reached by real-world graphs hardens the task of graph processing due to excessive memory demands even for distributed environments. By and large, such contemporary graph processing systems employ ineffective in-memory representations of adjacency lists. Therefore, memory usage patterns emerge as a primary concern in distributed graph processing. We seek to address this challenge by exploiting empirically-observed properties demonstrated by graphs generated by human activity. In this paper, we propose 1) three compressed adjacency list representations that can be applied to any distributed graph processing system, 2) a variable-byte encoded representation of out-edge weights for space-efficient support of weighted graphs, and 3) a tree-based compact out-edge representation that allows for efficient mutations on the graph elements. We experiment with publicly-available graphs whose size reaches two-billion edges and report our findings in terms of both space-efficiency and execution time. Our suggested compact representations do reduce respective memory requirements for accommodating the graph elements up–to 5 times if compared with state-of-the-art methods. At the same time, our memory-optimized methods retain the efficiency of uncompressed structures and enable the execution of algorithms for large scale graphs in settings where contemporary alternative structures fail due to memory errors.