Exploratory search: New name for an old hat?

explore Exploratory search has gained prominence in recent years. There is an increased interest from several scientific communities, from the information retrieval and database to human-computer interaction and visualization communities, in moving beyond the traditional query-browse-refine model supported by web search engines and database systems alike, and towards support for human intelligence amplification and information understanding. Exploratory search systems should inherently support symbiotic human-machine relationships that provide guidance in exploring unfamiliar information landscapes [Whi 09]. In this post, four researchers set to explore exploratory search from different angles and study the emerging needs and objectives as well as the challenges and problems that need to be tackled.

Exploratory Search in Structured Data: Data Warehouses and OLAP to the Rescue?

by Melanie Herschel
melanie

Looking more closely at what problems the field of exploratory search encompasses, Marchionini has established two main aspects of exploratory search that go beyond the classical problem of looking up information using classical search mechanisms [Mar 06]. More specifically, exploratory search has the general goal of (i) learning, thus acquiring new knowledge and (ii) investigating to possibly reveal new facts. Sub-tasks to achieve these goals include:

  • Learning: knowledge acquisition, comprehension and interpretation, comparison, aggregation and integration, and socialization.
  • Investigating: Accretion, analysis, synthesis, evaluation, discovery, planning and forecasting, and transformation.

Clearly, exploratory search applies to both structured and unstructured data, as we want to leverage as much data as possible in the above tasks. However, if we focus on structured data, we notice that quite a few of the tasks mentioned above resemble those for which data warehouses and online analytical processing (OLAP) have been developed. One example of a learning task supported by data warehouses is integration, as one of the main purposes of data warehouses is to integrate data from distributed and heterogeneous sources in a single repository. Consequently, data comparison is easily supported as well. As for supporting investigation tasks, OLAP and data mining algorithms readily support analysis of data stored in a data warehouse, and reports synthesize such data. Finally, data warehouses are deployed to support decision making, such as planning and forecasting.

So are the problems of exploratory search already solved for structured data through data warehouses and OLAP?

To some extent yes but there are still many areas of exploratory search that data warehouses do not cover, such as socialization, comprehension and interpretation, and discovery. In the following, I will highlight three main areas where I believe exploratory search will require novel approaches to reach its full potential.

Discovery. Data warehouses are designed based on a predefined information demand, meaning that the analytical queries they support are fixed in advance and for sure cannot go beyond queries that the schema of the data warehouse supports. Essentially, the data warehouse is designed to efficiently answer predefined queries. Opposed to that, in exploratory search, the information demand is unknown a priori, the goal of discovery being to also reveal things that users of the system did not think about during design time and thus are not covered by the rigid schema. Hence, system design for exploratory search needs to plan for the unplanned. Making an analogy to farming, we can say that whereas data warehouses and OLAP are useful for harvesting what has been planted, exploratory search is all about exploring and laboring new land.

Adaptation. Given the rigid cage schemas impose on a data warehouse, these systems typically have limited capabilities for changing or evolving information needs. But these are the essence of exploratory search, as learning is an iterative process that requires a search-refine-expand paradigm. That is, whereas data warehouses allow us to freely move in a cage, exploratory search systems need to be able to adapt to a new and rapidly changing environment.

Data and Users. As a final distinction here, we have limited the discussion above to structured data, but obviously, a wealth of information resides in unstructured data that exploratory search should natively support. This brings us back to a long going discussion on integrating databases and information retrieval. Additionally, many limitations of the data warehouse come from the rigid schema, so could maybe NoSQL databases be of help here? Finally, data warehouses and OLAP are targeted towards expert users that know their domain, whereas exploratory search is for the masses and human-computer interaction will be key to success.

To conclude, we have seen that data warehouses may support to some extent exploratory search, but they have serious limitations when it comes to discovery, adaptability, and the support of all types of relevant data and users.

Exploratory Search and Web searching

by Yannis Tzitzikas
yannisNew

Web searching is probably the most frequent user task in the Web, during which users typically get back a linear list of hits. In fact, web search engines are very good in ranking, and there is a plethora of ranking methods for capturing various aspects (topic relevance, authority, popularity, credibility and personalization). However, ranking is not enough. Ranking is adequate for focalized (else called precision-oriented) information needs, however the majority of information needs are recall-oriented (according to various user studies). For recall-oriented information needs, the user wants to find and understand more than one search hit. Examples include bibliographic survey writing, medical information seeking, patent search, hotel booking, and car buying. In general, recall-oriented information needs have an exploratory nature and aim at decision making (based on one or more criteria). To support such information needs, it is beneficial to provide methods that:

  • enable easy access to low ranked hits,
  • allow browsing the relevant hits and resources in groups (according to various criteria),
  • offer overviews (of variable complexity) of the hits, and
  • let the user to restrict gradually the search results.

The last years we, in the University of Crete and FORTH-ICS, have been investigating methods that can complement the classical Web searching with functionality for exploratory search. At first, we developed the web search engine (WSE) Mitos, a system which offers faceted search over the results of the submitted queries. Specifically, it supports facets corresponding to metadata attributes of the web pages (static metadata), as well as facets corresponding to the outcome of snippet-based clustering algorithms (a kind of dynamic metadata). The user can then restrict her focus gradually, by interacting with the resulting multidimensional structure through simple clicks.

The system IOS [Faf 12] was developed next, providing analogous features but during query typing (type-ahead search). This functionality is offered for the frequent queries and takes advantage of novel partitioned trie-based indexes for reducing the increased main memory requirements. Subsequently, through XSearch [Faf 12b] we investigated and showed how the available Linked Open Data (LOD) can be exploited for offering facets based on the outcome of entity mining techniques. Again, this approach is applied at query time over the textual snippets of the search hits, where LOD provides information about the names of the entities of interest. This approach has been proved successful for professional search, specifically in the domain of patent search and marine search. With X-ENS [Faf 12c] we focused on configurability, i.e., allowing the end user to configure the entities of interest, and also browse the information that LOD provides for these entities.

However, to offer entity mining not over the snippets, but over the full contents of the search hits, more than one machines are needed for downloading and analyzing the full contents of the hits. A recent work shows how MapReduce can be used for this purpose [Kit 14].

Subsequently, we investigated how we could enable the user to easily express preferences, simple and complicated ones, over the elements the user interacts with. Such preferences can affect the ordering of the different aspects of the multidimensional (and hierarchically organized) information space. We have developed the prototype system Hippalus that realized this approach over a proposed preference framework [Pap 14].

More information about the aforementioned systems is available here.

Based on our experience, we could identify four main topics that we believe current IR/Web search does not cover, and exploratory search will require to reach its full potential:

Ubiquity. Faceted browsing of search results and gradual restriction should be possible for any kind of query, for any domain, and with no predetermined facets. In other words, methods that bypass the need for explicit configuration (regarding facets, entity types, LOD sources) are required.

Fusion of Structured and Unstructured Content. The exploitation of LOD in the exploratory search process is promising, e.g. for Named Entity Recognition and disambiguation. However, the fusion of structured and unstructured content requires more work.

User Control. Explicit, user-provided, and controllable preference management is beneficial for supporting a transparent decision making process. We believe that the framework supported by the Hippalus system is a first step towards this direction.

Evaluation. We need easy-to-follow methods for evaluating the effectiveness of exploratory search methods, and easily reproducible evaluation results. Although the classical IR has well established methodologies for evaluation, things are not so clear and straightforward in interactive IR (IIR).

Exploratory Search and Multimedia Data

by K. Selcuk Candan
candan

By its very nature, multimedia data exploration shares the 3V challenges ([V]olume, [V]elocity, and[V]ariety ) of the so called “Big Data” applications. Systems supporting multimedia data exploration, however, must tackle additional, more specific, challenges, including those posed by the [H]igh-dimensional, [M]ulti-modal (temporal, spatial, hierarchical, and graph-structured), and inter-[L]inked nature of most multimedia data as well as the [I]mprecision of the media features and [S]parsity of the observations in the real-world.

Moreover, since the end-users for most multimedia data exploration tasks are us (i.e., humans), we need to consider fundamental constraints posed by [H]uman beings, from the difficulties they face in providing unambiguous specifications of interest or preference, subjectivity in their interpretations of results, and their limitations in perception and memory. Last, but not the least, since a large portion of multimedia data is human-centered, we also need to account for the users’ (and others’) needs for [P]rivacy.

Multimedia exploration (on data with the above characteristics and by users with the above limitations) is an inherently dynamic process, and systems for multimedia exploration must be able to support, efficiently and effectively, a continuous exploration cycle involving four key steps:

  • sense & integrate: the system takes as inputs and integrates data, media, and models of the application space and continuously sensed real-time media data,
  • filter, rank & recommend: the system provides support for context-aware access to integrated media data sets,
  • visualize & feedback: the system acquires accurate user feedback through an intuitive data and result representation, and
  • act & adapt: the system provides continuous adaptation of models of data, context, and user preference based on user feedback.

Naturally, each and every step of this multimedia exploration cycle poses significant challenges. While it would be impossible to enumerate all the challenges, I would like to identify the following five “core” challenges:

  • media annotation, summarization, and (dimensionality) reduction,
  • user, community, context, preference modeling and feedback,
  • multi-modal and richly structured/linked data exploration,
  • dynamic/evolving multimedia data exploration, and of course
  • bridging the semantic gap in media exploration.

Unfortunately, while as a community we have done great advances in tackling these core challenges, we probably have to admit that we are still quite far from addressing any of these issues satisfactorily, especially within the context of large multimedia data collections. In fact, at least in the short- to medium-term future, these five issues will continue to form the core challenges in multimedia exploration and retrieval.

If there is one thing that is becoming more urgent, however, it is that the ever-increasing scale and the speed of the data implies that to support the above media exploration cycle, our emphasis must shift towards development of integrated data platforms that can support, in an optimized and scalable manner, both media analysis (feature extraction, clustering, partitioning aggregation, summarization, classification, latent analysis) and data manipulation (filtering, integration, personalized and task-oriented retrieval) operations.

Personalizing Data Exploration: Exploring the Past

by Amelie Marian
amelie

Personal data is now pervasive as digital devices are capturing every part of our lives. Data is constantly collected and saved by users, either voluntarily in files, emails, social media interactions, multimedia objects, calendar items, contacts, etc., or passively by various applications such as GPS tracking of mobile devices, records of utility usage, financial transactions, or quantified self sensors. A typical user will have data recorded on many devices, cloud services, and proprietary systems; access to the data can be difficult because of the data fragmentation, security issues, or practical concerns.

Companies and organizations have famously been able to take advantage of the wealth of information produced by individuals, learning patterns and visualizing trends on a large number of data points produced by many users. Yet, individual uses do not have accessible tools to retrieve, manage and analyze their own data.

Leveraging personal data is critical to many data exploration tasks:

Exploring our past. A specific type of data exploration is re-finding [Tee 04], [Ber 08], whose goal is to find information that has been created, received, or seen by the user. Unlike traditional web search or exploration tasks whose focus is usually on discovering new information/documents, a re-finding exploration task has a target object it is attempting to recover.

Fragmentation of data is a main challenge. Users rarely own and store their personal data, with the exception of personal files. Most personal information is stored in the cloud by commercial companies that may offer some limited access, usually via a web browser or an API, to a user’s personal data. Attempting to retrieve and cross-reference personal information then leads to a tedious, often maddening, process of individually accessing all the relevant sources of data and manually linking their information. For example, checking that an insurance claim was correctly processed may require looking at a calendar application to find the doctor’s appointment date, checking the claim status on the insurance web site, and consulting a bank web portal to confirm that the payment was received.

The future of personal data exploration lies in the creation of personal data exploration tools, in the spirit of Dataspaces [Blu 07], [Hal 06], that will integrate personal data from a variety of sources, and allow users to visualize and search through their digital memories based on any piece of information they remember, following threads of information to navigate from one memory to the next.

Exploring our social data. Personal data is not limited to a user’s own data production. In an increasingly connected world, what other users in our social network share with us is also relevant to our data exploration. Users looking for book recommendations may want to explore their social network connections for books they have read or discussed. Some desktop search systems, such as deskWeb [Zer 10], have integrated the user’s social network graph, expanding the searched data set to include information available in the social network.

Personalizing our data exploration. Users have individual habits and patterns, as well as different types of data, context and information needs. By inferring knowledge from their past personal information and their interactions with their data, we can personalize data exploration and fit it to the users’ individual needs.

Social network information can be leveraged to learn contextual information about a user and guide data exploration. For instance, “Student Center” has different meaning for different groups of users. Knowing which social group the user belongs to can help us identify entities, e.g., observing that a majority of the user’s friends go to Rutgers University and attend events at “Busch Campus Student Center, Rutgers University”, increases the likelihood that the phrase “Student Center” in the user’s data and queries refers to that particular student center. Similarly, past queries can be used to learn some information about the type of the entities present in the personal data, or about the personal context.

This type of personalized data exploration is gaining traction in prospective memory applications, such as Google Now, which focus on reminding users of their appointments and to-do items.

Overall, understanding and leveraging personal data is crucial for many data exploration tasks, either because the exploration is on the personal information itself, or because personal information can give significant insights and directions for traditional data exploration needs.

References

[Whi 09] Ryen W. White and Resa A. Roth. Exploratory Search: Beyond the Query-Response Paradigm. Synthesis Lectures on Information Concepts, Retrieval, and Services, 2009, Vol. 1, No. 1 , Pages 1-98

[Mar 06] Gary Marchionini. Exploratory search: from finding to understanding. Communications of the ACM. Volume 49 Issue 4, April 2006, Pages 41-46.

[Faf 12] Pavlos Fafalios, Ioannis Kitsos, Yannis Tzitzikas: Scalable, flexible and generic instant overview search. WWW 2012: 333-336.

[Faf 12b] Pavlos Fafalios, Ioannis Kitsos, Yannis Marketakis, Claudio Baldassarre, Michail Salampasis, Yannis Tzitzikas: Web Searching with Entity Mining at Query Time. IRFC 2012: 73-88.

[Faf 12c] Pavlos Fafalios, Yannis Tzitzikas: X-ENS: semantic enrichment of web search results at real-time. SIGIR 2013: 1089-1090.

[Pap 14] Panagiotis Papadakos, Yannis Tzitzikas: Hippalus: Preference-enriched Faceted Exploration. EDBT/ICDT Workshops 2014: 167-172.

[Kit 14] Ioannis Kitsos, Kostas Magoutis, Yannis Tzitzikas, Scalable entity-based summarization of web search results using MapReduce, Journal on Distributed and Parallel Databases, 32(3), 2014, 405-446.

[Tee 04] Jaime Teevan, Christine Alvarado, Mark S. Ackerman, and David R. Karger. The perfect search engine is not enough: a study of orienteering behavior in directed search. In CHI, pages 415–422, 2004.

[Ber 08] Ofer Bergman, Ruth Beyth-Marom, Rafi Nachmias, Noa Gradovitch, and Steve Whittaker. Improved search engines and navigation preference in personal information management. ACM Trans. Inf. Syst., 26(4):20:1–20:24, October 2008.

[Blu 07] Lukas Blunschi, Jens peter Dittrich, Olivier Ren Girard, Shant Kirakos, Karakashian Marcos, and Antonio Vaz Salles. A dataspace odyssey: The imemex personal dataspace management system. In CIDR, 2007.

[Hal 06] Alon Halevy, Michael Franklin, and David Maier. Principles of dataspace systems. Communications of the ACM, 2006. 

[Zer 10] Sergej Zerr, Elena Demidova, and Sergey Chernov. deskweb2.0: Combining desktop and social search. In Proc. of Desktop Search Workshop, In conjunction with the 33rd Annual International ACM SIGIR 2010, 23 July 2010, Geneva, Switzerland, 2010.

Blogger Profiles:
Melanie Herschel is an Associate Professor in the Computer Science Department of the University of Paris South and is also member of the INRIA Oak project. Before this, after receiving her Ph.D. in Computer Science from Humboldt-University Berlin (2008), she was a postdoctoral researcher at IBM Research – Almaden (2008 – 2009) and at the University of Tübingen, Germany (2009 – 2011). During her research career, she has focused on topics related to data integration, data quality, data provenance, and, more recently, on processing large amounts of Web Data. Melanie participates in several nationally funded research projects and is an active member of the database research community, as she has served on numerous program committees, is a frequent journal referee, and has organized international workshops.

Yannis Tzitzikas is Assistant Professor in the Department of Computer Science of the University of Crete and Research Associate of FORTH-ICS. His research interests fall in the intersection of the following areas: Information Systems, Information Indexing and Retrieval, Conceptual Modeling, Knowledge Representation and Reasoning. He is one of the editors (and author of several chapters) of the book “Dynamic Taxonomies and Faceted Search” (Springer 2009), he is currently involved in the ongoing EU projects i-Marine, SCIDIP-ES, Aparsen NoW (WP leader), and he is national contact of the MUMIA (Multilingual and Multifaceted Interactive Information Access) Cost Action. His current research revolves around Faceted Interactive Search, and lately on methods that can bridge the world of documents with the world of data at search time. He has published more than 80 papers in refereed international conferences and journals (including ACM Transactions on the Web, VLDB Journal, Journal on Data Semantics, International Semantic Web Conference (winner of best paper award), ESWC, and many others).

K. Selçuk Candan is a Professor of Computer Science and Engineering at the Arizona State University. He has published over 170 journal and peer-reviewed conference articles, one book on multimedia retrieval, and 16 book chapters. He has 9 patents. Prof. Candan served as an associate editor of one of the most respected database journals, the Very Large Databases (VLDB) journal. He is also in the editorial board of the IEEE Transactions on Multimedia and the Journal of Multimedia. He has served in the organization and program committees of various conferences. In 2006, he served as an organization committee member for SIGMOD’06, the flagship database conference of the ACM. In 2008, he served as a PC Chair for another leading, flagship conference of the ACM, this time focusing on multimedia research (MM’08). More recently, he served as a program committee group leader for ACM SIGMOD’10. He also serves in the review board of the Proceedings of the VLDB Endowment (PVLDB). In 2011, he served as a general co-chair for the ACM MM’11 conference. In 2012 he served as a general co-chair for ACM SIGMOD’12. He has successfully served as the PI or co-PI of numerous grants, including from the National Science Foundation, Air Force Office of Research, Army Research Office, Mellon Foundation, HP Labs, and JCI. He also served as a Visiting Research Scientist at NEC Laboratories America for over 10 years. He is a member of the Executive Committee of ACM SIGMOD and an ACM Distinguished Scientist.

Amélie Marian is an Associate Professor in the Computer Science Department at Rutgers University. Her research interests are in Personal Information Management, Ranked Query Processing, Semi-structured data and Web data Management. Amélie received her Ph.D. in Computer Science from Columbia University in 2005. From March 1999 to August 2000, she was a member of the VERSO project at INRIA-Rocquencourt. She is the recipient of a Microsoft Live Labs Award, three Google Research Awards, and an NSF CAREER award.

Is Query Optimization a “Solved” Problem?

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Is Query Optimization a “solved” problem? If not, are we attacking the “right” problems? How should we identify the “right” problems to solve?

I asked these same questions almost exactly 25 years ago, in an extended abstract for a Workshop on Database Query Optimization that was organized by the then-Professor Goetz Graefe at the Oregon Graduate Center [Grae 89a]. Remarkably and quite regrettably, most of the issues and critical unsolved problems I identified in that brief rant remain true today. Researchers continue to attack the wrong problems, IMHO: they attack the ones that they can, i.e., that they have ideas for, rather than the ones that they should, i.e., that are critical to successfully modeling the true cost of plans and choosing a good one. Perhaps more importantly, that will avoid choosing a disastrous plan! At the risk of repeating myself, I’d like to re-visit these issues, because I’m disappointed that few in the research community have taken up my earlier challenge.

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The root of all evil, the Achilles Heel of query optimization, is the estimation of the size of intermediate results, known as cardinalities. Everything in cost estimation depends upon how many rows will be processed, so the entire cost model is predicated upon the cardinality model. In my experience, the cost model may introduce errors of at most 30% for a given cardinality, but the cardinality model can quite easily introduce errors of many orders of magnitude! I’ll give a real-world example in a moment. With such errors, the wonder isn’t “Why did the optimizer pick a bad plan?” Rather, the wonder is “Why would the optimizer ever pick a decent plan?”

“Well,” you say, “we’ve seen lots of improvements in histograms, wavelets, and other statistics since 1989. Surely we do better now.” There’s been no shortage of such papers, it’s true, but the wealth of such papers precisely illustrates my point. Developing new histograms that improve selectivity estimation for individual local predicates of the form “Age BETWEEN 47 AND 63” by a few percent doesn’t really matter, when other, much larger errors that are introduced elsewhere in cardinality estimation dwarf those minor improvements. It’s simple engineering, folks. If I have to review one more such paper on improved histograms for local predicates, I’ll scream (and reject it)! It just doesn’t matter! What we have now is good enough.

What still introduces the most error in cardinality estimation is (a) host variables and parameter markers, (b) the selectivity of join predicates, and, even more significantly, (c) how we combine selectivities to estimate the cardinality. Amazingly, these three topics also have enjoyed the least research attention, or at least the fewest number of papers attempting to solve them, unless I’ve missed some major contributions lately. I’ll visit each of these topics in turn, describing the fundamental causes of errors, and why those errors can easily reach disastrous proportions, illustrated by war stories from real customer situations.

Host variables and parameter markers
Host variables, parameter markers, and special registers occur in SQL queries because applications on top of the DBMS, not humans, invoke most queries, unlike in academic papers. Such applications typically get the constants used in predicates from a “fill in the blank” field on a web page, for example. The SQL predicate then looks like “Age BETWEEN :hv1 AND :hv2”. At compile time, the optimizer has no clue what :hv1 and :hv2 are, so it cannot look them up in those wonderful histograms that we all have polished. Instead, it must make a wild guess on the average or likely values, which could be off significantly from one execution to the next, or even due to skew. A war story illustrates this point.

One of our major ISVs retrofitted a table with a field that identified which subsystem each row came from. It had 6 distinct values, but 99.99% of the rows had the value of the founding subsystem, i.e., when there was only one. A predicate on this subsystem column was added to every query, with the value being passed as a host variable. Not knowing that value a priori, DB2’s optimizer used the average value of 1/|distinct values| = 0.167, though that predicate’s true selectivity was usually 0.9999 (not selective at all) and once in a blue moon was 0.0001 (extremely selective).

There has been some work on this so-called Parametric Query Optimization (PQO), though it’s sometimes attacking the problem of other parameters unknown at compilation time (e.g. the number of buffer pages available) or limited to discrete values [Ioan 97]. One of my favorites is a fascinating empirical study by Reddy and Haritsa [Redd 05] of plan spaces for several commercial query optimizers as the selectivity of multiple local predicates are varied. It demonstrated (quite colorfully!) that regions in which a particular plan is optimal may not be convex and may even be disconnected! Graefe suggested keeping a different plan for each possible value of each host variable [Grae 89b], but with multiple host variables and a large number of possible values, Graefe’s scheme quickly gets impractical to optimize, store, and decide at run-time among the large cross-product of possible plans, without grouping them into regions having the same plan [Stoy 08].

Version 5 of DB2 for OS/390 (shipped June 1997) developed a practical approach to force re-optimization for host variables, parameter markers, and special registers by adding new SQL bind options REOPT(ALWAYS) and REOPT(ONCE). The latter re-optimizes the first time that the statement is executed with actual values for the parameters, and assumes that these values will be “typical”, whereas the former forces re-optimization each time the statement is run. Later, a REOPT(AUTO) option was added to autonomically determine if re-optimization is needed, based upon the change in the estimated filter factors from the last re-optimization’s plan.

Selectivity of join predicates
The paucity of innovation in calculating join predicate selectivities is truly astounding. Most extant systems still use the techniques pioneered by System R for computing the selectivity of a join as the inverse of the maximum of the two join-column cardinalities [Seli 79], which essentially assumes that the domain of one column is a subset of the other. While this assumption is valid for key domains having referential integrity constraints, in general the overlap of the two sets may vary greatly depending upon the semantics of the joined domains. Furthermore, the common practice of pre-populating a dimension table such as “Date” with 100 years of dates into the future can incorrectly bias this calculation when the fact table initially has only a few months of data. Statistics on join indexes [Vald 87] would give much more precise selectivities for join predicates, if we were willing to suffer the added costs of maintenance and lock contention of updates to these join indexes. However, join indexes would not solve the intersection problem typical of star schemas.

For example, suppose a fact table of Transactions has dimensions for the Products, Stores, and Dates of each transaction. Though current methods provide accurate selectivity estimates for predicates local to each dimension, e.g., ProductName = ‘Dockers’ and StoreName = ‘San Jose’ and Date = ’23-Feb-2013’, it is impossible to determine the effect of the intersection of these predicates on the fact table. Perhaps the San Jose store had a loss-leader sale on Dockers that day that expired the next day, and a similar sale on some other product the next day, so that the individual selectivities for each day, store, and product appear identical, but the actual sales of Dockers on the two days would be significantly different. It is the interaction of these predicates, through join predicates in this case, that research to date doesn’t address. This leads naturally to the final and most challenging problem in our trifecta.

Correlation of columns
With few exceptions ([Chri 83], [Vand 86]), query optimizers since [Seli 79] have modeled selectivities as probabilities that a predicate on any given row will be satisfied, then multiplied these individual selectivities together. Effectively, this assumes that Prob{ColX = Value1, ColY = Value2} = Prob{ColX = Value1} * Prob{ColY = Value2}, i.e., that the two predicates are probabilistically independent, if you recall your college probability. Fortunately for the database industry, this assumption is often valid. However, occasionally it is not.

My favorite example, which occurred in a customer database, is Make = ‘Honda’ and Model = ‘Accord’. To simplify somewhat, suppose there are 10 Makes and 100 Models. Then the independence (and uniformity) assumption gives us a selectivity of 1/10 * 1/100 = 0.001. But since only Honda makes Accords, by trademark law, the real selectivity is 0.01. So we will under-estimate the cardinality by an order of magnitude. Such optimistic errors are much worse than pessimistic over-estimation errors, because they cause the optimizer to think that certain operations will be cheaper than they really are, causing nasty surprises at run time. The only way to avoid such errors is for the database administrator to be aware of the semantic relationship (a functional dependency, in this case) between those two columns and its consequences, and to collect column group statistics, as DB2 and other database products now allow.

To identify these landmines in the schema automatically, Stillger et al. [Stil 01] developed the LEarning Optimizer (LEO), which opportunistically and automatically compared run-time actual cardinalities to optimizer estimates, to identify column combinations exhibiting such correlation errors. Ilyas et al. [Ilya 04] attacked the problem more pro-actively in CORDS (CORrelation Detection by Sampling), searching somewhat exhaustively for such correlations between any two columns in samples from the data before running any queries. And Markl and colleagues [Mark 05], [Mark 07] have made ground-breaking advances on a consistent way to combine the selectivities of conjuncts in partial results.

All great progress on this problem, but none yet solves the problem of redundant predicates that can be inadvertently introduced by the query writer who typically believes that “more is better”, that providing more predicates helps the DBMS do its job better – it’s American as Apple Pie! Let me illustrate with one of my favorite war stories.

At a meeting of the International DB2 User’s Group, a chief database administrator for a major U.S. insurance company whom I’d helped with occasional bad plans asked me to conduct a class on-site. I suggested it include an exercise on a real problem, unrehearsed. After my class, she obliged me by presenting two 1-inch stacks of paper, each detailing the EXPLAIN of a plan for a query. I feared I was going to embarrass myself and fail miserably under the gun. The queries differed in only one predicate, she said, but the original ran in seconds whereas the one with the extra predicate took over an hour.

I instinctively examined first the cardinality estimates for the results of the two, and the slower one had a cardinality estimate 7 orders of magnitude less than the fast one. When asked what column the extra predicate was on, my host explained that it was a composite key constructed of the first four letters of the policy-holder’s last name, the first and middle initials, the zip code, and last four digits of his/her Social Security Number. Did the original query have predicates on all those columns? Of course! And how many rows were there in the table? Ten million. Bingo! I explained that that predicate was completely redundant of the others, and its selectivity, 1/107, when multiplied by the others, underestimated the cardinality by 7 orders of magnitude, wreaking havoc with the plan. It took me maybe 5 minutes to figure this out, and I was immediately dubbed a “genius”, but it really was straightforward: the added predicate might help the run-time, especially if they had an index on that column, but it totally threw off the optimizer, which couldn’t possibly have detected that redundancy without LEO or CORDS.

So c’mon, folks, let’s attack problems that really matter, those that account for optimizer disasters, and stop polishing the round ball.

Disclaimer: The postings on this site are my own and don’t necessarily represent IBM’s positions, strategies or opinions.

References

[Chri 83] S. Christodoulakis, “Estimating record selectivities”, Info. Systems 8,2 (1983) pp. 105-115.

[Grae 89a] G. Graefe, editor, Workshop on Database Query Optimization, CSE Technical Report 89-005, Oregon Graduate Center, Portland, OR, 30 May 1989.

[Grae 89b] G. Graefe, “The stability of query evaluation plans and dynamic query evaluation plans”, Proceedings of ACM-SIGMOD, Portland, OR (1989).

[Ioan 97] Y. Ioannidis et al., “Parametric query optimization”, VLDB Journal, 6(2), pp. 132 – 151, 1997.

[Ilya 04] I. F. Ilyas, V. Markl, P. J. Haas, P. Brown, A. Aboulnaga: CORDS: Automatic Discovery of Correlations and Soft Functional Dependencies. SIGMOD Conference 2004: 647-658.

[Mark 05] V. Markl, N. Megiddo, M. Kutsch, T. Minh Tran, P. J. Haas, U. Srivastava: Consistently Estimating the Selectivity of Conjuncts of Predicates. VLDB 2005: 373-384.

[Mark 07] V. Markl, P. J. Haas, M. Kutsch, N. Megiddo, U. Srivastava, T. Minh Tran: Consistent selectivity estimation via maximum entropy. VLDB J. 16(1): 55-76 (2007)

[Redd 05] N. Reddy and J. R. Haritsa. “Analyzing plan diagrams of database query optimizers”, VLDB, 2005.

[Seli 79] P.G. Selinger, M.M. Astrahan, D.D. Chamberlin, R.A. Lorie, and T.G. Price, “Access path selection in a Relational Database Management System”, Procs. Of ACM-SIGMOD (1979), pp. 23-34.

[Stil 01] M. Stillger, G. M. Lohman, V. Markl, M. Kandil: LEO – DB2′s LEarning Optimizer. VLDB 2001: 19-28.

[Stoy 08] J. Stoyanovich, K. A. Ross, J. Rao, W. Fan, V. Markl, G. Lohman: ReoptSMART:A Learning Query Plan Cache. Technical report.

[Vald 87] P. Valuriez, “Join indices”, ACM Trans. on Database Systems 12, 2 (June 1987), pp. 218-246.

[Vand 86] B.T. Vander Zanden, H.M. Taylor, and D. Bitton, “Estimating block accesses when attributes are correlated, Procs. of the 12th Intl. Conf. on Very Large Data Bases (Kyoto, Sept. 1986), pp. 119-127.

Blogger’s Profile:
Dr. Guy M. Lohman is Manager of Disruptive Information Management Architectures in the Advanced Information Management Department at IBM Research Division’s Almaden Research Center in San Jose, California, where he has worked for 32 years. He currently manages the Blink research project, which contributed BLU Acceleration to DB2 for Linux, UNIX, and Windows (LUW) 10.5 (GA’d 2013) and the query engine of the IBM Smart Analytics Optimizer for DB2 for z/OS V1.1 and the Informix Warehouse Accelerator products (2007-2010). Dr. Lohman was the architect of the Query Optimizer of DB2 LUW and was responsible for its development from 1992 to 1997 (versions 2 – 5), as well as the invention and prototyping of Visual Explain, efficient sampling, the DB2 Index Advisor, and optimization of XQuery queries in DB2. Dr. Lohman was elected to the IBM Academy of Technology in 2002 and made an IBM Master Inventor in 2011. He was the General Chair for ACM’s 2013 Symposium on Cloud Computing and is the General Co-Chair of the 2015 IEEE International Conference on Data Engineering (ICDE). Previously, he was on the editorial boards of the “Very Large Data Bases Journal” and “Distributed and Parallel Databases”. He is the author of over 75 papers in the refereed academic literature, and has been awarded 39 U.S. patents. His current research interests involve disruptive machine architectures for Business Intelligence, advanced data analytics, query optimization, self-managing database systems, information management appliances, database compression, and autonomic problem determination.

Systems & Databases: Let’s Break Down the Walls

phil
After hanging out exclusively with the database community for 35+ years, I’ve recently become more involved with the systems research community. I have a few observations and recommendations to share.Much of the work published in systems conferences covers topics that would have a natural home in database conferences. For example, transactions and data streams are currently in vogue in the systems field. Here are some recent Best-Paper-award topics: data streams at SOSP 2013, data-parallel query processing at OSDI 2008, and transactions OSDI 2012 and SOSP 2007. Although this topic overlap is increasing lately, it is not just a recent phenomenon. In the last 10+ years, the systems community has taken an interest in data replication, fault tolerance, key-value stores, data-parallel processing (i.e., map-reduce), query processing in sensor networks, and record caching. Some of this work has had a major impact on the database field, e.g., map-reduce and multi-master replication.Yet despite this overlap of topics, there has been remarkably little attendance of database researchers at systems conferences or of systems researchers at database conferences. Not a good thing. And until 2013, I’ve been part of the problem.

To help break down this barrier, SIGMOD and SIGOPS have sponsored the annual Symposium on Cloud Computing (SoCC) since 2010. Personally, I’ve found attending SoCC’s to be time well spent, and I had a good experience attending ICDCS 2013 in July. So I decided to attend my first SOSP in October, 2013. It was great. The fraction of that program of direct interest to me was as large as any database conference, including work on transactions, data streams, replication, caching, fault tolerance, and scalability. I knew there wouldn’t be a huge number of database attendees, but I wasn’t prepared to be such an odd duck. At most a dozen attendees out of 600 were card-carrying members of the database community. With few database friends to hang out with, I got to meet a lot more new people than I would at a database event and hear about projects in my areas that I’d wouldn’t otherwise have known about. Plus, I could advertise my own work to another group of folks.

If you do systems-oriented database research, then how about picking one systems conference to attend this year? NSDI in Seattle in April, ICDCS in Madrid in late June, or OSDI in Colorado in October. And if you see a systems researcher looking lonely at a database conference, then strike up a conversation. If they feel welcome, perhaps they’ll tell others and we’ll see more systems attendees at database events.

Given the small overlap of attendees in systems and database conferences, we shouldn’t be surprised that the communities have developed different styles of research papers. Since systems papers typically describe mechanisms lower on the stack, they often focus on broader usage scenarios. They value the “ities” more than database papers, e.g., scalability, availability, manageability, and security. They favor simple ideas that work robustly over complex techniques that report improvements for some inputs. They expect a paper to work through the system details in a credible prototype, and to report on lessons learned that are more broadly applicable. They expect more micro-benchmarks that explain the source of performance behavior that’s observed, rather than just benchmarks that model usage scenarios, such as TPC.

From attending systems conferences, serving on SoCC program committees (PC’s), and submitting an ill-fated paper to a systems conference, I learned a few things about systems conferences that we database folks could learn from:

1. More of their conferences are single-track, and they leave more time for Q&A. This leads to more-polished presentations. When you’re presenting a paper to 600 attendees, doing it badly is a career-limiter.

I’ve always liked CIDR, not only because of its system-building orientation, but also because it’s single-track. I’m forced to attend sessions on topics I normally would ignore, and hence I learn more. I think we should try making one of our big conferences single-track: SIGMOD, VLDB or ICDE. One might argue that too many excellent papers are submitted, so it’s impractical. I disagree. Here’s one way to do it: Have the same acceptance rate as in the past, and all papers are published as usual. However, the PC selects only one-track’s worth of papers for presentation slots. The other papers are presented in poster sessions that have no competing parallel sessions. The single-track enables us to learn about more topics, the density of great presentations is higher, and the poster sessions enable us to dig deep on papers of interest to us (as some of our DB conferences already enable us to do). Unless our field stops growing, we really have to do something like this. Our conferences have already gotten out of hand with as many as seven parallel sessions.

2. When describing related work, systems papers cast a wider net. Their goal is often not simply to demonstrate that the paper’s contributions are novel, but also to educate the reader about lines of work that are loosely related to the paper’s topic. To help enable this breadth, they’ve recently settled on a formula where references don’t count toward the paper’s maximum page count. We should adopt this view in the database community. One self-serving benefit is that papers would cite more references, which would increase all of our citation counts.

3. There’s a widespread belief that systems PC’s write longer, more constructive reviews. I think DB PC’s have been improving in this respect, but on average we’re not up to the systems’ standard.

4. They usually shepherd all papers, to ensure that authors incorporate recommended changes. This also gives the authors someone to ask about ambiguous recommendations. We should do this too.

5. They typically require 10pt font. As a courtesy to those of us over a certain age, whose eyes aren’t what they used to be, we should do this, and increase the page count to maintain the same paper length.

6. At SOSP and OSDI, they post all slide decks and videos of all presentations. Obviously worthwhile. We sometimes post slide decks and videos only for plenaries. Let’s do better.

There are a few aspects of systems conferences that I’m less enthusiastic about.

1. Systems conferences favor live PC meetings. They do have benefits. It’s educational for PC members to hear discussions of papers they didn’t review and for young researchers to see how decisions are reached. Since all PC members hear every paper discussion, non-reviewers can bring up points that neutralize inappropriate criticisms or raise issues that escaped reviewers’ attention, which reduces the randomness of decisions. However, there are also negatives. In borderline cases, the most articulate, quick-thinking, and extroverted PC members have an inappropriate advantage in getting their way. And inevitably, some PC members can’t attend the meeting, due to a schedule conflict or the travel expense, so the papers they reviewed get short shrift.

On balance, I don’t think the decisions produced by live PC meetings are enough better than on-line discussions to be worth what they cost. Perhaps we can get some of the benefits of a PC meeting by allowing PC members to see the reviews and discussions of all papers for which they don’t have a conflict, late in the discussion period (which we did some years ago). With large PC’s, this might constitute public disclosure, in which case patents would have to be filed before submission, which will delay the publication of some work. But if we think a broader vetting of papers is beneficial, this might be worth trying.

2. During the reviewing process of a systems conference, if a paper seems borderline, then the PC chairs solicit more reviews. These reviews do offer a different perspective on the interest-value of the work. But they are usually not as detailed as the initial ones and may be by less-expert reviewers. It’s common to receive 5 or 6 reviews of a paper submitted to a systems conference. As an author it’s nice to get this feedback. And it might reduce the randomness of decisions. But it significantly increases the reviewing load on PC’s. In database PC’s, we rarely, if ever, do this. In my opinion, it’s usually better to hold reviewers’ feet to the fire and make them decide. Still, we’d benefit from getting 4 or 5 reviews more often than never, but not as often as systems conferences.

3. Systems conferences publish very few industry papers and rarely have an industry track. In my experience, an industry paper is judged just like a research paper, but with a somewhat lower quality bar. This is unfortunate. Researchers and practitioners benefit from reading about the functionality and internals of state-of-the-art products, even if they are only modestly innovative, especially if they are widely used. The systems community would serve their audience better by publishing more such papers.

4. The reviewing load for systems PC’s is nearly double that of DB PC’s, so systems PC’s are proportionally smaller. E.g., SOSP 2013 had 160 submissions and 28 PC members. If each paper had three reviews, that’s 17 reviews per PC member. With five reviews/paper, that’s 29 reviews per PC member. If DB conferences cranked up the reviewing load, then we’d probably participate in fewer PC’s, so the workload on each of us might be unchanged. A PC member would see more of the submissions in each area of expertise, which might help reduce the randomness of decisions … maybe. Overall, I don’t see a compelling argument to change, but it’s debatable.

5. Like some DB conferences, many systems conferences have adopted double-blind reviewing. I am not a fan. For papers describing a system project, I find it hard to review and, in some cases, impossible to write a paper for a double-blind conference. I have chosen not to submit some papers to double-blind conferences. I know I am not alone in this.

In summary, the systems and DB areas have become much closer in recent years. Each community has something to learn from the other area’s technical contributions and point-of-view and from its approach to conferences and publications. We really would benefit from talking to each other a lot more than we do.

Acknowledgments: My thanks to Gustavo Alonso, Surajit Chaudhuri, Sudipto Das, Sameh Elnikety, Mike Franklin, and Sergey Melnik for contributing some of the ideas in this blog post.

Blogger’s Profile:
Philip A. Bernstein is a Distinguished Scientist at Microsoft Research. Over the past 35 years, he has been a product architect at Microsoft and Digital Equipment Corp., a professor at Harvard University and Wang Institute of Graduate Studies, and a VP Software at Sequoia Systems. During that time, he has published papers and two books on the theory and implementation of database systems, especially on transaction processing and data integration, which are still the major focus of his research. He is an ACM Fellow, a winner of the ACM SIGMOD Innovations Award, a member of the Washington State Academy of Sciences and a member of the National Academy of Engineering. He received a B.S. degree from Cornell and M.Sc. and Ph.D. from University of Toronto.

Are Databases ready for Gestural Workloads?

arnab

In 2011, I was traveling on the Kolkata Metro when I made an interesting observation. Nearly every passenger around me was using a computing device of some sort. Some were playing with their smartphones, some with their tablets, some listening to music on their iPod Touches. Each was a consumer of structured data of some sort; interacting with their email, Facebook, or music catalog. Weeks later, while on a flight, this pattern came up again — every seat in the plane was equipped with a touch-driven entertainment system, some used while sifting through book pages on e-Readers. Clearly, the notion of a “computer” for the future generation looks very different from the past!

Over time, we seem to be converging to a vision where we are surrounded by computing devices of varying capacities, with users performing both simple and complex tasks, with a common attribute — these devices do not possess keyboards. In 2011, smartphones and tablets outsold workstations and portable computers by 1.5x. In 2013, this ratio is projected to reach 4x. Based on this trend, non-keyboard interaction (typically in the form of gestures) is on-track to be the dominant mode of data interaction.

growth

Gesture-driven applications pose a very different workload to underlying databases than traditional ones. Consider the idea of scrolling through a page of text, which is typically implemented as a SELECT / LIMIT query for each page, with page transitions implemented as successive queries. Most touch and gestural interfaces now implement inertial scrolling, where the speed of transitions can be superlinear. With a few successive swipes, the frontend could easily overload the query queue with successive SELECT / LIMIT queries, one for each page. Ironically, due to the high speed of scrolling, most of these results are moving too fast, and are hence ignored by the user. So, the question arises: Are today’s databases capable of handling gestural workloads? If not, what parts of the database should we fix? Should we devise a new query scheduler that can rapidly reprioritize / kill queries? (e.g., pages that the user has skipped no longer need to be queried or returned) Should we investigate bounded-latency execution? (e.g., the application needs results within Xms) Or should we change the query paradigm altogether? (e.g., query for a preview of results while scrolling is in motion, and then detailed results as the view stabilizes)

Another example of an interesting gestural interaction is that of the Interactive Join. As part of the GestureDB system we are developing in my group, the GestureQuery multitouch interface lets you compose two relations into an equijoin by simply dragging them together on the specific attributes:

In the interactive join, assuming all attributes are of the same data type, there could be M x N possible JOIN combinations, but only one is the correct intended query. We model this gesture as a query intent, a probability distribution over the space of possible queries(i.e. the M x N possible joins). Initially, the likelihood of each query is uniform, but while the user articulates the gesture, we can use the distance between attributes to compute the probability of each query.

The following interactive demo lets you play around with a real multitouch interaction trace from one of our user studies, collected from interactions with our GestureQuery iPad prototype2. The subject was asked to perform an equijoin Album.ArtistId with Artist.ArtistId, dragging the two tables close to each other. Dragging the slider left to right lets you step through the recorded trace. Notice how rapidly the “Proximity Score” changes during the interaction.

This interaction is an great example of query intent transition. As the gesture is being articulated, the distribution changes rapidly. The “Proximity Score” is the score for Album.ArtistId ⋈= Artist.ArtistId, the inverse of the distance between the two tables, which can be considered the likelihood for the intent — there is a score for each pair of attributes. Thus, in order to provide a preview for the intended join, this would involve quickly executing and displaying results while the user is articulating the gesture, basing the priority on these scores.

Just like the previous example, challenges arise here. Can a database provide join results at such low latency for such a diverse number of queries being fired at the same time, given such rapidly changing probabilities? It is not practical for the database to execute them all in parallel. Given that we know the domain of possible queries, is there scope for multiquery optimization? Second, given an interactive threshold of say 100ms for the results, is it feasible (or even useful) to generate full results for all queries? Should we consider partial query execution? Or can we simply sample the tables to provide interactive (albeit inaccurate) results? Third, feedback for a query is useful only for a fleeting moment during the gesture articulation. How could we show parts (i.e. rank tuples) of the result that are most useful?

Clearly, when working with gesture-driven interfaces and application layers, the gestural workloads of database queries pose a smorgasbord of challenges, that might require rethinking of the entire database stack. As these interfaces evolve, the underlying database systems will have to keep up with changes in user expectations and demands. With the explosion of new and novel gestural interfaces, natural interfaces and casual computing lately, it’s an exciting time for database research!

References:
1. Arnab Nandi: Querying Without Keyboards: CIDR 2013 (Vision Track)
2. Lilong Jiang, Arnab Nandi, Michael Mandel: GestureQuery: A Multitouch Database Query Interface: VLDB 2013 (demo)

Blogger’s Profile:
Arnab Nandi is an Assistant Professor of Computer Science & Engineering at The Ohio State University. Arnab’s research is in the area of database systems, focusing on challenges in big data analytics and data interaction. The goal of his group’s work is to allow end-users to explore and interact with semi-structured and structured data. Prior to joining Ohio State, Arnab did his Ph.D. at the University of Michigan, Ann Arbor, with stints at Google, Yahoo! Research, and Microsoft Research.

The Curses of Heterogeneity in Big Data

KLerman

Both theoretical and empirical research may be unnecessarily complicated by failure to recognize the effects of heterogeneity” – Vaupel & Yashin

Big Data is daily topic of conversation among data analysts, with much said and written about its promises and pitfalls. The issue of heterogeneity, however, has received scant attention. This is unfortunate, since failing to take heterogeneity into account can easily derail the discoveries one makes using these data.

This issue, which some may recognize as an example of ecological fallacy, first came to my attention via a paper elegantly titled “Heterogeneity’s ruses: some Surprising Effects of Selection on Population Dynamics” (Vaupel and Yashin, 1985). Authors discuss a variety of examples where the aggregated behavior of a heterogeneous population, composed of two homogeneous but differently behaving subpopulations, will differ from the behavior of any single individual. Consider the following example. It has been observed that the recidivism rate of convicts released from prison declines with time. A natural conclusion one may reach from this observation is that former convicts are less likely to commit crime as they age. However, this is false. In reality, there may be two groups of individuals “reformed” and “incorrigible” with constant – but different – recidivism rates. With time, there will be more “reformed” individuals left in the population, as the “incorrigibles” are sent back to prison, resulting in decreasing recidivism rate for the population as a whole. This simple example shows that “the patterns observed [at population level] may be surprisingly different from the underlying patterns on the individual level. Researchers interested in uncovering these individual patterns, perhaps to help develop or test theories or to make predictions, might benefit from an “understanding of heterogeneity’s ruses.” (Vaupel & Yashin)

My colleagues and I have been tricked by heterogeneity time and again. As one example, our study of information spread on the follower graphs of Twitter and Digg revealed that it was surprisingly different from the simple epidemics that are often used to model information spread. In a simple epidemic, described, for example, as an independent cascade model, the probability of infection increases monotonically with the number of exposures to infected friends. This probability is measured by the exposure response function. The figure below shows the exposure response function we measured on Twitter: the probability for becoming infected (i.e., retweet) information (a URL) on Twitter as a function of how many friends had previously tweeted this information. In contrast to epidemics, it appears as though repeated exposure to information suppresses infection probability. We measured an even more pronounced suppression of infection on Digg [Ver Steeg et al, 2011], and a similar exposure response was observed for hashtag adoption following friends’ use of them [Romero et al, 2011].

Figure: Exposure response on Twitter. Probability a user will retweet a message containing a URL after a number of friends have tweeted about it. Retweet probability is averaged over all users.
figure1

It is easy to draw wrong conclusions from this finding. In “What stops social epidemics?” [Ver Steeg et al, 2011], we reported that information spread on Digg is quickly extinguished, and attributed this to the exposure response function. We speculated that initial exposures “inoculate” users to information, so that they will not become infected (i.e., propagate it) despite multiple exposures. Now we know this explanation was completely wrong.

Figure: Exposure response of subpopulations of Twitter users, differentiated according to the number of friends they follow.
figure2

The exposure response function, while aggregated over all users, does not describe the behavior of any individual Digg or Twitter user – even the hypothetical “typical” user. In fact, there is no “typical” Twitter (or Digg) user. Twitter users are extremely heterogeneous. Separating them into more homogeneous sub-populations reveals a more regular pattern. Figure 2 shows the exposure response function for different populations of Twitter users, separated according to the number of friends they follow (large fluctuations are the result of small sample size). Why number of friends? This is explained in more detail in our papers [Hodas & Lerman 2012, 2013], but in short, we found it useful to separate users according to their cognitive load, i.e., the volume of information they receive, which is (on average) proportional to the number of friends they follow [Hodas et al, 2013]. Now, the probability that a user within each population will become infected increases monotonically with the number of infected, very similar to the predictions of the independent cascade model.

Figure 2 has a different, more significant interpretation, with consequences for information diffusion. It suggests that highly connected users, i.e., those who follow many others, are less susceptible to becoming infected. Their decreased susceptibility in fact explains Figure 1: as one moves to the right of the exposure response curve, only the better connected, and less sensitive, users contribute to that portion of the response. However, despite their reduced susceptibility, highly connected users respond positively to repeated exposures, like all other users. You do not inhibit response by repeatedly exposing people to information. Instead, the reason that these users are less susceptible hinges on the human brain’s limited bandwidth. There are only so many tweets any one can read, the more tweets you receive (on average proportional to the number of friends you follow), the less likely you are to see – and retweet – any specific tweet. If it was not for recognizing heterogeneity, we would not have found this far more interesting explanation.

References

  • Vaupel, J. W. and Yashin, A. I. (1985). Heterogeneity’s ruses: some surprising effects of selection on population dynamics. The American Statistician, 39(3):176-185.
  • Hodas, N. O. and Lerman, K. (2013). The simple rules of social contagion.
  • Hodas, N. O., Kooti, F., and Lerman, K. (2013). Friendship paradox redux: Your friends are more interesting than you. In Proceedings of 7th International Conference on Weblogs and Social Media.
  • Steeg, G. V., Ghosh, R., and Lerman, K. (2011). What stops social epidemics? In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media.
  • Romero, D. M., Meeder, B., and Kleinberg, J. (2011). Differences in the mechanics of information Diffusion Across topics: Idioms, political hashtags, and Complex Contagion on twitter. In Proceedings of World Wide Web Conference.
Blogger’s Profile:
Kristina Lerman is a Project Leader at the University of Southern California Information Sciences Institute and holds a joint appointment as a Research Associate Professor in the USC Computer Science Department. After a brief stint as a theoretical roboticist, she found her calling in blending together methods from physics, computer science and social science to address problems in social computing and social media analysis. She writes many papers that are greatly enjoyed by all of their twenty readers.