OpenRaiser/Intern-Atlas huggingface dataset:
StaticGreedy: solving the scalability-accuracy dilemma in influence maximization
Authors: Suqi Cheng, Huawei Shen, Junming Huang, Guoqing Zhang, Xueqi Cheng
Venue: CIKM
Year: 2013

Influence maximization, defined as a problem of finding a set of seed nodes to trigger a maximized spread of influence, is crucial to viral marketing on social networks. For practical viral marketing on large scale social networks, it is required that influence maximization algorithms should have both guaranteed accuracy and high scalability. However, existing algorithms suffer a scalability-accuracy dilemma: conventional greedy algorithms guarantee the accuracy with expensive computation, while the scalable heuristic algorithms suffer from unstable accuracy In this paper, we focus on solving this scalability-accuracy dilemma. We point out that the essential reason of the dilemma is the surprising fact that the submodularity, a key requirement of the objective function for a greedy algorithm to approximate the optimum, is not guaranteed in all conventional greedy algorithms in the literature of influence maximization. Therefore a greedy algorithm has to afford a huge number of Monte Carlo simulations to reduce the pain caused by unguaranteed submodularity. Motivated by this critical finding, we propose a static greedy algorithm, named StaticGreedy, to strictly guarantee the submodularity of influence spread function during the seed selection process. The proposed algorithm makes the computational expense dramatically reduced by two orders of magnitude without loss of accuracy. Moreover, we propose a dynamical update strategy which can speed up the StaticGreedy algorithm by 2-7 times on large scale social networks.
Flexible and extensible generation and corruption of personal data
Authors: Peter Christen, Dinusha Vatsalan
Venue: CIKM
Year: 2013

With much of today's data being generated by people or referring to people, researchers increasingly require data that contain personal identifying information to evaluate their new algorithms. In areas such as record matching and de-duplication, fraud detection, cloud computing, and health informatics, issues such as data entry errors, typographical mistakes, noise, or recording variations, can all significantly affect the outcomes of data integration, processing, and mining projects. However, privacy concerns make it challenging to obtain real data that contain personal details. An alternative to using sensitive real data is to create synthetic data which follow similar characteristics. The advantages of synthetic data are that (1) they can be generated with well defined characteristics; (2) it is known which records represent an individual created entity (this is often unknown in real data); and (3) the generated data and the generator program itself can be published. We present a sophisticated data generation and corruption tool that allows the creation of various types of data, ranging from names and addresses, dates, social security and credit card numbers, to numerical values such as salary or blood pressure. Our tool can model dependencies between attributes, and it allows the corruption of values in various ways. We describe the overall architecture and main components of our tool, and illustrate how a user can easily extend this tool with novel functionalities.
Modeling clicks beyond the first result page
Authors: Aleksandr Chuklin, Pavel Serdyukov, Maarten de Rijke
Venue: CIKM
Year: 2013

Most modern web search engines yield a list of documents of a fixed length (usually 10) in response to a user query. The next ten search results are usually available in one click. These documents either replace the current result page or are appended to the end. Hence, in order to examine more documents than the first 10 the user needs to explicitly express her intention. Although clickthrough numbers are lower for documents on the second and later result pages, they still represent a noticeable amount of traffic.
Breast and prostate cancer expression similarity analysis by iterative SVM based ensemble gene selection
Authors: Darius Coelho, Lee Sael
Venue: CIKM
Year: 2013

Epidemiologic and phenotypic evidences indicate that breast and prostate cancers have high pathological similarities. Analysis of pathological similarities between cancers can be beneficial in several aspects such as enabling the knowledge transfer between the cancer studies. To gain knowledge of the similarity between the breast and prostate cancer pathology, common genes that are affected by the two carcinomas are investigated. Gene expression data extracted from RNA-seq experiments, provided through TCGA consortium, is used for gene selection. Gene selection was performed using an iterative SVM based ensemble feature selection approach. Iterative SVM-based gene selection methods enable correlated gene expressions to be considered simultaneously and ensemble approach stabilizes the selection. As results of the analysis, two genes, Transglutaminase 4 (TGM4) and complement component 4A (C4A), were selected as commonly altered genes. Direct relationships of the two genes to the two cancers are not confirmed. However, TGM4 is known to be associated with adenocarcinomas and C4A with ovarian cancer. Thus provides evidence that they maybe pathologically important genes for the two cancers.
Exploring the effectiveness of medical entity recognition for clinical information retrieval
Authors: James Cogley, Nicola Stokes, Joe Carthy
Venue: CIKM
Year: 2013

The growth of medical and clinical textual datasets has fostered research interests in methods for storing, retrieving and extracting of pertinent data. In more recent years, shared tasks and more comprehensive data sharing agreements have seen a further growth in the research area spanning Natural Language Processing (NLP) and Information Retrieval (IR) to aid the world of healthcare. Frequently NLP applications such as Medical Entity Recognition (MER), are motivated within the context of improving IR system performance. In this paper, we investigate the application of MER to a clinical retrieval system in the context of shared tasks in the respective areas. Namely, we aim to add structure to previously unstructured clinical reports and query sets. We evaluate the performance of MER on the query set, highlighting issues in constructing queries in a clinical setting. Further to this, we evaluate the performance of structuring queries on a retrieval dataset. We find that while structuring queries improves performance on complex queries that contain many term dependencies, there is a larger issue of linguistic variation found in clinical texts that must also be addressed.
Enriching the web by modeling reading difficulty
Authors: Kevyn Collins-Thompson
Venue: CIKM
Year: 2013

The ability to read and understand a text would seem to be a basic aspect of interacting with a rich information source like the Web, yet little is currently known about the nature of the Web, its users, and how users interact with content when seen through the lens of reading difficulty. For example, a document isn't relevant to a person's information need - at least, not immediately - if they can't understand it, yet Web search engines have traditionally ignored the problem of finding or providing content at the right level of difficulty as an aspect of relevance. I'll give an overview of recent research that shows how computing and applying metadata based on text readability at Web scale opens up new and sometimes surprising possibilities for enriching our interactions with the Web: from personalizing Web search results, to predicting user and site expertise, to estimating searcher motivation. I'll also highlight future challenges and opportunities in improving text readability analysis, particularly in light of the rapidly growing interest in large-scale applications for online education.
UMicS: from anonymized data to usable microdata
Authors: Graham Cormode, Entong Shen, Xi Gong, Ting Yu, Cecilia M. Procopiuc, Divesh Srivastava
Venue: CIKM
Year: 2013

There is currently a tug-of-war going on surrounding data releases. On one side, there are many strong reasons pulling to release data to other parties: business factors, freedom of information rules, and scientific sharing agreements. On the other side, concerns about individual privacy pull back, and seek to limit releases. Privacy technologies such as differential privacy have been proposed to resolve this deadlock, and there has been much study of how to perform private data release of data in various forms. The focus of such works has been largely on the data owner: what process should they apply to ensure that the released data preserves privacy whilst still capturing the input data distribution accurately. Almost no attention has been paid to the needs of the data user, who wants to make use of the released data within their existing suite of tools and data. The difficulty of making use of data releases is a major stumbling block for the widespread adoption of data privacy technologies.
Maintaining discriminatory power in quantized indexes
Authors: Matt Crane, Andrew Trotman, Richard A. O&apos, Keefe
Venue: CIKM
Year: 2013

The time cost of searching with an inverted index is directly proportional to the number of postings processed and the cost of processing each posting. Dynamic pruning reduces the number of postings examined. Pre-calculation then quantization of term / document weights reduces the cost of evaluating each posting. The effect of quantization on precision, latency, and index size is examined herein. We show empirically that there is an ideal size (in bits) for storing the quantized scores. Increasing this adversely affects index size and search latency; decreasing it adversely affects precision. We observe a relationship between the collection size and ideal quantization size, and provide a way to determine the number of bits to use from the collection size.
Search excavator: the knowledge discovery tool
Authors: Dmitri Danilov, Eero Vainikko
Venue: CIKM
Year: 2013

We present a knowledge discovery tool Search Excavator (SE) developed for detecting similar words in web documents ranked by overall usage frequency in American English. The SE prototype application is a web browser add-on developed to assist users in acquiring new knowledge in unknown domains and to help in posing more specific search queries. The SE is designed to discover similar but generally infrequent words with surrounding texts in browser web documents and then suggest found words as possible query keywords. This technique allows users to discover unknown data intersections and use less ambiguous queries to target the required documents. The SE concept is motivated by a number of ideas. The similar infrequent words in the texts of the relevant documents can include field specific terms and facts that can be unknown to the user. Suggesting such keywords can decrease the overall search time encouraging early learning by directing users to the new unknown relevant terms and facts in a search session with an ambiguous query. Finally, we present four demonstration scenarios from our small-scale qualitative user study of the SE tool.
pEDM: online-forecasting for smart energy analytics
Authors: Lars Dannecker, Philipp Rösch, Ulrike Fischer, Gordon Gaumnitz, Wolfgang Lehner, Gregor Hackenbroich
Venue: CIKM
Year: 2013

Continuous balancing of energy demand and supply is a fundamental prerequisite for the stability of energy grids and requires accurate forecasts of electricity consumption and production at any point in time. Today's Energy Data Management (EDM) systems already provide accurate predictions, but typically employ a very time-consuming and inflexible forecasting process. However, emerging trends such as intra-day trading and an increasing share of renewable energy sources need a higher forecasting efficiency. Additionally, the wide variety of applications in the energy domain pose different requirements with respect to runtime and accuracy and thus, require flexible control of the forecasting process. To solve this issue, we introduce our novel online forecasting process as part of our EDM system called pEDM. The online forecasting process rapidly provides forecasting results and iteratively refines them over time. Thus, we avoid long calculation times and allow applications to adapt the process to their needs. Our evaluation shows that our online forecasting process offers a very efficient and flexible way of providing forecasts to the requesting applications.
Manipulation among the arbiters of collective intelligence: how wikipedia administrators mold public opinion
Authors: Sanmay Das, Allen Lavoie, Malik Magdon-Ismail
Venue: CIKM
Year: 2013

Our reliance on networked, collectively built information is a vulnerability when the quality or reliability of this information is poor. Wikipedia, one such collectively built information source, is often our first stop for information on all kinds of topics; its quality has stood up to many tests, and it prides itself on having a "Neutral Point of View". Enforcement of neutrality is in the hands of comparatively few, powerful administrators. We find a surprisingly large number of editors who change their behavior and begin focusing more on a particular controversial topic once they are promoted to administrator status. The conscious and unconscious biases of these few, but powerful, administrators may be shaping the information on many of the most sensitive topics on Wikipedia; some may even be explicitly infiltrating the ranks of administrators in order to promote their own points of view. Neither prior history nor vote counts during an administrator's election can identify those editors most likely to change their behavior in this suspicious manner. We find that an alternative measure, which gives more weight to influential voters, can successfully reject these suspicious candidates. This has important implications for how we harness collective intelligence: even if wisdom exists in a collective opinion (like a vote), that signal can be lost unless we carefully distinguish the true expert voter from the noisy or manipulative voter.
Aligning freebase with the YAGO ontology
Authors: Elena Demidova, Irina Oelze, Wolfgang Nejdl
Venue: CIKM
Year: 2013

Linked Open Data (LOD) has emerged as the de-facto standard for publishing data on the Web. The cross-domain large scale Freebase and YAGO datasets represent central hubs and reference points for the LOD cloud. Freebase is an open-world dataset, which contains about 22 million entities and more than 350 million facts in more than 100 domains. The scale of Freebase makes it difficult for the users to get an overview of the data and efficiently retrieve the desired information. Integration of Freebase with the YAGO ontology that contains more than 360,000 concepts enables us to provide more semantic information for Freebase and to facilitate novel applications, such as efficient query construction, over large scale data. In this paper we analyze the structure of YAGO in more depth and show how to match YAGO and Freebase categories. The new YAGO+F structure that results from our matching tightly connects both datasets and provides an important next step to systematically interconnect LOD subcollections. We make our YAGO+F structure available online in the hope that it can provide a good starting point for future applications, which can build upon a wide variety of Freebase data clearly arranged in the semantic categories of YAGO.
A new operator for efficient stream-relation join processing in data streaming engines
Authors: Roozbeh Derakhshan, Abdul Sattar, Bela Stantic
Venue: CIKM
Year: 2013

In the last decade, Stream Processing Engines (SPEs) have emerged as a new processing paradigm that can process huge amounts of data while retaining low latency and high-throughputs. Yet, it is often necessary to join streaming data with traditional databases to provide more contextual information for the end-users and applications. The major problem that we confront is to join the fast arriving stream tuples with the static relation tuples that are on a slow database. This is what we call the Stream-Relation Join (SRJ) problem. Currently, SPEs use a naive tuple-by-tuple approach for SRJ processing where the SPE accesses the database for every incoming tuple. Some SPEs use cache to avoid accessing the database for every incoming tuple, while others do not because of the stochastic nature of streaming data. In this paper, we propose a new SRJ operator to facilitate SRJ processing regardless of the cache performance using two techniques: batching and out-of-order processing. The proposed operator provides an effective generic solution to the SRJ problem and the cost of incorporating our operator into different SPEs is minimal. Our experiments use a variety of synthetic and real data sets demonstrating that our operator outperforms the state-of-the-art tuple-by-tuple approach in terms of maximizing the throughput under ordering and memory constraints.
Document sublanguage clustering to detect medical specialty in cross-institutional clinical texts
Authors: Kristina Doing-Harris, Olga V. Patterson, Sean Igo, John F. Hurdle
Venue: CIKM
Year: 2013

This paper reports on a set of studies designed to identify sublanguages in documents for domain-specific processing across institutions. Psychological evidence indicates that humans use context-specific linguistic information when they read. Natural Language Processing (NLP) pipelines are successful within specific domains (i.e., contexts). To limit the number of domain-specific NLP systems, a natural focus would be on sublanguages. Sublanguages are identified by shared lexical and semantic features.[1] Patterson and Hurdle[2] developed a sublanguage identification system that functioned well for 12 clinical specialties at the University of Utah. The current work compares sublanguages across institutions. Using a clinical NLP pipeline augmented by a new document corpus from the University of Pittsburg (UPitt), new documents were assigned to clusters based on the minimum cosine-distance to a Utah cluster centroid. The UPitt documents were divided into a nine-group specialty corpus. Across institutions, five of the specialty groups fell within the expected clusters. We find that clustering encounters difficulty due to documents with mixed sublanguages; naming convention differences across institutions; and document types used across specialties. The findings indicate that clinical specialty sublanguages can be identified across institutions.
On challenges with mobile e-health: lessons from a game-theoretic perspective
Authors: Ann-Marie Eklund
Venue: CIKM
Year: 2013

Health portals play an important role in today's health care, and the increased mobility places demands on the portals to provide as accurate and few suggestions as possible. Often the information seekers may be in distress, lacking medical knowledge and expressing themselves in ways that make it difficult for the portal to interpret the seekers' needs. This raises the question on how portal providers may be able both to better model, or describe, the user behaviour and to predict the impact of changes in search algorithms to address these challenges.
Applying theory to practice
Authors: Ronald Fagin
Venue: CIKM
Year: 2013

We discuss the art of applying theory to practice. In particular, we discuss in detail our interactions with two research projects at IBM Almaden: the Garlic project, which built a multimedia database system on top of various existing systems, and the Clio project, which developed tools for converting data from one format to another. We discuss the problems we resolved, and the impact this had both on the Garlic or Clio systems and on the broader scientific community. We draw morals from these interactions, including why theoreticians do better theory by working with system builders, and why system builders build better systems by working with theoreticians. We present the remarkably simple Threshold Algorithm, which is optimal in an extremely strong sense: optimal not just in the worst case, or in the average case, but in every case! The Threshold Algorithm and its variants have applications to numerous areas, including information retrieval, fuzzy and uncertain databases, group recommendation systems, and the semantic web .
Exploring weakly supervised latent sentiment explanations for aspect-level review analysis
Authors: Lei Fang, Minlie Huang, Xiaoyan Zhu
Venue: CIKM
Year: 2013

In sentiment analysis, aspect-level review analysis has been an important task because it can catalogue, aggregate, or summarize various opinions according to a product's properties. In this paper, we explore a new concept for aspect-level review analysis, latent sentiment explanations, which are defined as a set of informative aspect-specific sentences whose polarities are consistent with that of the review. In other words, sentiment explanations best represent a review in terms of both aspect and polarity. We formulate the problem as a structure learning problem, and sentiment explanations are modeled with latent variables. Training samples are automatically identified through a set of pre-defined aspect signature terms (i.e., without manual annotation on samples), which we term the way weakly supervised.
Active exploration: simultaneous sampling and labeling for large graphs
Authors: Meng Fang, Jie Yin, Xingquan Zhu
Venue: CIKM
Year: 2013

Modern information networks, such as social networks, are often characterized with large sizes and dynamic changing structures. To analyze these networks, existing solutions commonly rely on graph sampling techniques to reduce network sizes, and then carry out succeeding mining processes, such as labeling network nodes to build classification models. Such a sampling-then-labeling paradigm assumes that the whole network is available for sampling and the sampled network is useful for all subsequent tasks (such as network classification). Yet real-world networks are rarely immediately available unless the sampling process progressively crawls every single node and its connections. Meanwhile, without knowing the underlying analytic objective, the sampled network can hardly produce quality results. In this paper, we propose an Active Exploration framework for large graphs where the goal is to carry out network sampling and node labeling at the same time. To achieve this goal, we consider a network as a Markov chain and compute its stationary distribution by using supervised random walks. The stationary distribution of the sampled network help identify important nodes to be explored in the next step, and the labeling process labels the most informative node which in turn strengthens the sampling process. The mutually and simultaneously enhanced sampling and labeling processes ensure that the final network contains a maximum number of nodes directly related to the underlying mining tasks.