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USENIX 2003 Annual Technical Conference, General Track — Paper    [USENIX Annual Conference '03 Tech Program Index]

Pp. 99-112 of the Proceedings
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A Logic File System

Yoann Padioleau and Olivier Ridoux
IRISA / University of Rennes 1
Campus universitaire de Beaulieu
35042 RENNES cedex, FRANCE


On the one hand, hierarchical organizations are rigid in the sense that there is only one path to each document. On the other hand, keyword-based search is flexible because many sets of keywords may lead to the same document, but it lacks a navigation mechanism. We present the new paradigm of a logic file system, which integrates navigation and classification, and the possibility of expressive queries. This paradigm associates logical descriptions to files, and logical deduction serves as a basis for navigation and querying; paths are formulas. A key notion is the extension of a logical formula: i.e., the set of all files whose description satisfies the formula. The root directory is determined by formula $\mathit{true}$, and sub-directories of a directory are determined by formulas whose extension strictly intersects the directory extension. This gives a logical ground for considering navigation as computing relevant hints to help refining a query. A prototype implementation demonstrates encouraging performances.


Computer users can access a very large variety and number of digital documents. Managing so many documents is a difficult task. An important difficulty is to find quickly one desired document among many files; this is the information retrieval problem. Information retrieval techniques cannot be thought independently of organization methods; they form paradigms. The most well-known information retrieval/organization paradigms are the hierarchical paradigm, the boolean paradigm, and the relational paradigm. This introduction continues by presenting the advantages and disadvantages of these paradigms. The Logic File System is an original combination of these three paradigms.

Hierarchical organizations

Traditional file systems belong to the hierarchical paradigm, as well as Web portals such as Yahoo!, and many e-mail managers. Information is organized by first creating a hierarchy of concepts, which induces a tree-like structure, and then putting objects in this tree, which is the classification process. Information is searched by going up and down in this tree; this is the navigation process. In a file system, concepts are directories, and objects are files.

The advantages of the hierarchical paradigm are:
$\bullet  $ the system proposes navigation hints to the user, e.g., directory names. If the user does not remember or does not know the exact classification of an object, then the computer will help him.
$\bullet  $ the system proposes relevant hints only. By first proposing global concepts and then sub-concepts, the system helps in finding an object step-by-step.

The disadvantages are:
$\bullet  $ since there is a single path to a file, one must know it exactly. This requires either luck or erudition. Gopal and Mander say [8] ``Beyond a certain scale limit, people cannot remember locations for explicit path names, after so many years, it is still amusing to see even experienced UNIX system administrators spend time trying /usr/lib or was it /usr/local/lib, maybe /opt/local/etc/lib or /opt/unsupported/lib. There are of course many search tools available, but organizing large file systems is still too hard''. Our objective is to take into account this human factor; users of a file system tend to look for files that they do not know where they are. So, users should not commit themselves to expressing definite paths.
$\bullet  $ one can put an object in only one place, which means one cannot associate several independent (i.e., not ordered by the subconcept relation) concepts to one object. However, one often wants to associate several independent concepts to an object: e.g., price and weight.
$\bullet  $ another disadvantage is that the logic of the query language is limited to conjunctive formulas (e.g., $a/b$ for traditional file systems), though disjunction is a clean formal way of expressing non-definite paths. Disjunction can be simulated using shell tricks, such as in
  ls -r */(keyword1|keyword2)/ ,
but cd */(keyword1|keyword2)/ means nothing. Negation is still more difficult to simulate. To express non-definite paths, a user would like to do just cd !keyword1, or cd keyword1|keyword2.

Boolean organizations

Web search engines such as Google belong to the boolean paradigm. Information (in the Web case, HTML files) is organized by describing information elements with keywords. One finds an information by specifying the keywords that its description must contain. It can be extended by boolean formulas, such as
\vee \mathit{keyword}_2 \wedge \mathit{keyword}_3$.

The advantages of the boolean paradigm are:
$\bullet  $ As opposed to the hierarchical paradigm, it is easy to describe an information element by a conjunction of concepts. It is flexible because of the many boolean formulas that are equivalent. E.g., queries $\mathit{man}\wedge\mathit{ps}$ and $\mathit{ps}\wedge\mathit{man}$ are equivalent, as opposed to a hierarchical file systems where man/ps and ps/man are not equivalent at all.
$\bullet  $ The query language is expressive; it permits conjunction, disjunction and negation.

The disadvantages are:
$\bullet  $ As opposed to the hierarchical paradigm, the system does not answer a query with relevant keywords; instead, it returns a list of all information elements whose description is partially matched by the query. So, the exact keywords that permit to select an information element must be guessed by the user; the system does not help.
$\bullet  $ Since the system answers with a list of information elements the result of a query can be long, which can mean useless, because a user cannot/does not want to go through the entire list to find the desired file. That means that the user needs to find further keywords without help.

So, one would like that a boolean system proposes relevant keywords that will refine the search; one wants a navigation capability with a boolean query capability.

Relational organizations

Databases belong to the relational paradigm. Data is organized by first creating tables, according to a pre-established schema, and then by filling in those tables with items. One searches an object by using a complex language based on relational algebra.

The big advantage of databases is the expressiveness of their query language. E.g., one can associate valued attributes to items, and then make complex queries such as
select ... where size > 45 .

The difficulty is that this expressiveness comes at a price. Databases are more complex systems than file systems, and are less easy to learn (a UNIX-shell course: 1 hour, an SQL course: 40 hours). This is because there are many concepts to learn, such as selection and projection. Moreover, as for boolean organizations, it does not propose navigation, which makes it difficult to find an information without knowing the schema of the database.

A new paradigm

All paradigms described in this introduction have their advantages and drawbacks. The main contribution of our work is to propose a new organization which makes it possible to combine navigation, querying, ease of use, and expressiveness, and which can be implemented reasonably efficiently. As opposed to previous works (see Section 6 for related works), the combination of querying and navigating is unrestricted. In particular, one can navigate in the result of any query. This organization is an instantiation of a theoretical approach [3,4] that generalizes file paths to almost arbitrary logic formulas. Information systems based on this approach are called Logic Information Systems (LIS for short). Once an organization is chosen, the question remains of what will be its place in a system architecture. The proposed organization is implemented as a file system. This makes it usable by all sorts of applications, e.g., shells, editors, compilers, multimedia players. The file system will be called Logic File System (LISFS for short, and to avoid confusion with the Log-structured File System [16]).

The article is organized as follows. We first present the principles and usage of a logic file system in Section 2. Then we expose an implementation scheme, with its data structures and algorithms in Section 3. We present additional features and extensions to the basic scheme in Section 4. Section 5 describes an actual implementation and the results of experiments. Section 6 presents related works. Finally, we present future search directions and conclusions in Sections 7 and 8 .

Principles of a logic file system

We first describe a file system based on the boolean query paradigm and then we extend it to add the navigation capability. In terms of Gopal and Mander example (see Section 1.1), the boolean file system allows to do ls lib to get every library files. With navigation, the answer is not formed of all library files, but of all keywords that can be used to make the query more precise: in their example, usr, opt, ... Then, the user can do ls lib/usr, and the answer will be all files that are fully identified by keywords lib and usr (in fact, the files of UNIX directory /usr/lib), plus keyword local, because it helps identifying files that are in usr/lib/local and not in usr/lib. Keyword opt is not listed because it is not relevant to lib/usr. In this framework, `/' commutes, so that usr/lib and lib/usr are equivalent.

The boolean file system plus the navigation capability forms the core of the logic file system. We present the semantics of shell commands in the resulting file system. Then we explain how this new paradigm affects the security model. We describe a file system in terms of shell commands, because they are more user-oriented. Only when entering into deeper details in Section 3.3, do we consider actual file system operations, like lookup and readdir.

A boolean file system

As we have chosen a standard file system interface we have to deal with files and directories. Boolean properties will play the role of directories. To handle boolean queries in a file system, we associate a conjunction of properties of interest (i.e., a directory) to every file. This is done by first registering property names with command mkdir, e.g., mkdir man; mkdir latex;... At this stage, this indicates that man and latex are subdirectories of /, but it also indicates that man/latex and latex/man are paths to potential subdirectories. Second, command cd sets the working directory (variable PWD in some shells) to a desired list of property names. Finally, commands like touch and cat will create files associated with the properties named in the PWD, e.g., cd man/latex/french; touch myfile. Note that latex needs not be a subconcept of man, nor french be a subconcept of latex, as in a hierarchical file system. The slash (/) must be read as a conjunction, hence it is commutative.

Boolean logic defines a language, i.e., its formulas, and an entailment relation that is usually written $\models$. $f_1\models f_2$ means that if $f_1$ is true, then $f_2$ must be true too. For instance, $a\wedge b\models a$ holds in boolean logic. In the current prototype, formulas must be presented in conjunctive normal form: e.g., $a$, $\neg a$, $a_1\vee a_2\vee a_3$, or $(a_1\vee a_2)\wedge
a_3\wedge (\neg a_4)$. These formulas are written a, !a, a1|a2|a3, and (a1|a2)&(a3)&(!a4) in the concrete syntax. The entailment relation is that of usual boolean logic.

Figure 1: a boolean file system
\begin{figure}\centerline{ \psfig{figure=booleanFS.eps,width=3.125in}
} \end{figure}

Let ${\cal O}$ be the set of all the files in the file system, $d(o)$ be the description of a file $o$ (in our case, $d(o)$ is a conjunction of properties), $name(o)$ be the name of file $o$, and $c(o)$ be the content of file $o$. The state of a boolean organization is well-represented by a $\mathit{name}\times\mathit{property}$ matrix (see Figure 1 for an illustration). The answer to ls in a PWD $p$ is $\{ name(o) \mid o\in{\cal O},
d(o)\models p\}$. This set is called the extension of formula $p$. Note the risk of name clashes in extensions; several $o$'s with different descriptions, but same name, may clash in an extension. This risk will disappear in the final design of Section 2.2. The root directory, or /, is equivalent to formula $\mathit{true}$. So, doing ls at the root will list the names of all the files in the system, because anything entails $\mathit{true}$.

The PWD evolves incrementally. Given a property x, doing cd x in a PWD p, changes the PWD into $\mbox{\mbox{\texttt{p}}}\wedge\mbox{\mbox{\texttt{x}}}$. A logic path must always be composed with the PWD, taking into account relative and absolute paths, and special names like "..". E.g., doing cd /y sets PWD to y.

As files evolve, their descriptions evolve too. So, one needs a way to update the description of a file. In hierarchical file systems, the place where a file is located is the current description of this file, and when one wants to modify its description, one just changes its location using command mv. This works in the same way in a boolean file system. Concretely, if one executes mv p1/f1 p2/f2, the effect is to change the name of f1 into f2, and to change its description, deleting attributes of p1 and adding those of p2. E.g., assume file f is created with description $\mbox{\mbox{\texttt{man}}}\wedge\mbox{\mbox{\texttt{latex}}}\wedge\mbox{\mbox{\texttt{french}}}$, then command cd /man/ps; mv f ../pdf results in the new description $\mbox{\mbox{\texttt{man}}}\wedge\mbox{\mbox{\texttt{pdf}}}\wedge\mbox{\mbox{\texttt{latex}}}$ for file f.

Executing rmdir x removes a property name (i.e., a virtual directory). It first checks that x is a simple atom (neither a disjunction, a conjunction, nor a negation), and that this property is empty, i.e.,  $\{ o \mid o\in{\cal O}, d(o) \models \mbox{\mbox{\texttt{x}}} \}=\emptyset$. Finally, rm proceeds as in a hierarchical file system.

At this stage, a boolean file system has all the advantages, but also the drawbacks, of a boolean organization. In the following section, we add navigation to the boolean file system. This defines the full logic file system.

Boolean file system, plus navigation

The answer to a boolean query can be very large (typically, cd /; ls would list the extension of the root, i.e., every file), so one would like to add a navigation capability to the boolean file system. So doing, the system will answer a query with relevant properties that refine the search. Those properties will be presented as subdirectories. By ``relevant'' we mean that if in a PWD $p$ all files have keyword $a_1$, some have the keyword $a_2$, and none have the keyword $a_3$, then neither $a_1$ nor $a_3$ is relevant to distinguish between the files, but $a_2$ is relevant. Command ls lists sub-directories, but also files that cannot be distinguished any further by relevant properties. In a logic file system, the files located in a PWD $p$ are the files whose description satisfies $p$, but does not satisfy any of its subdirectories. In the preceding example, if one of the files had just the properties mentioned in $p$ and $a_1$, and nothing more (i.e., not $a_2$), then it would be listed.

More formally, let ${\cal F}$ be the set of all property names, let $\mathit{ext}(f) = \{ o \in{\cal O} \mid d(o)\models f \}$ (the extension of $f$), then the answer to ls in a PWD $p$ is divided in two parts, the sub-directories $\mathit{Dirs}$, and the files $\mathit{Files}$, such that
  $\mathit{Dirs} = \{ f \in {\cal F} \mid \emptyset\subset
\mathit{ext}(f\wedge p)\subset \mathit{ext}(p) \}$
  $\mathit{Files} = $
  $\{ o\in{\cal O} \mid
d(o)\models p, \neg\exists f\in \mathit{Dirs} \mid d(o) \models f\wedge p\}$
(see Figure 2 for an illustration). Sub-directories in $\mathit{Dirs}$ are also called increments.

Figure 2: querying and navigating
} \end{figure}

In hierarchical file systems, several files may have exactly the same name, provided that they are not located in the same directory; otherwise, the user would not be able to disambiguate which file he wants to manipulate. In the boolean file system, the same problem arises, so one must ensure that if two files have the same name, they must not have exactly the same description. If this condition is kept true, a path always exists where only one of these two files is listed, and so where there is no ambiguity. Navigation always finds this path. For instance, users Alice and Bob may both have a file called foo; one file foo will have attribute alice, and the other one will have bob. Alice in her homedir /home/alice will see her own foo, and Bob in his homedir /home/bob will see his own foo. A file foo may also have attributes alice and bob; in this case, it is visible by both users.

Navigation in a taxonomy

With this scheme, the number of file names listed by a command ls is reduced, but there may be too many increments anyway. For instance, at the root directory, the system would list nearly all the property names. The principle of navigation is obviously to propose concepts that refine the search, but also to propose the most general such concepts. E.g., assuming computer science documents, one would like to classify the keywords so that the system first proposes the main fields of computer science, e.g., algorithms, databases, operating-systems, and then the subfields, e.g., UNIX, Linux, Windows.

So, one needs some means for stating that an atomic property is more general than others. To this aim, we use the mkdir command to create a hierarchy of concepts: a taxonomy. So, to say that a property $a_1$ is more specific than $a_0$, one simply executes mkdir $a_0$; cd $a_0$; mkdir $a_1$, as for files. If an object $x$ has property $a_1$, then it must also have property $a_0$. In logical terms, this means that $d(x)\models a_1$ must entail $d(x)\models a_0$. So, the extension of $a_0$ must contain the extension of $a_1$. This is achieved by considering the taxonomic relations as axioms, e.g.,  $a_1\models a_0$. Note that the taxonomic relation is a directed acyclic graph (a DAG). E.g., doing
cd /OperatingSystem/TradeMark
mkdir Unix
makes Unix a subconcept of OperatingSystem (axiom $\mbox{\mbox{\texttt{Unix}}}\models\mbox{\mbox{\texttt{OperatingSystem}}}$), and of TradeMark (axiom $\mbox{\mbox{\texttt{Unix}}}\models\mbox{\mbox{\texttt{TradeMark}}}$1). Now, if a user does ls /OperatingSystem all files with property Unix will also satisfy this query.

Command ls is refined accordingly, by proposing the most general increments. So, the answer to ls in a PWD $p$ becomes
  $\mathit{Dirs} = \mathit{max}_{\models}(\{ f\in{\cal F} \mid \emptyset\subset \mathit{ext}(f \wedge p) \subset \mathit{ext}(p)\})$,
  $\mathit{max}_{\models}({\cal F}) = \{ f \in{\cal F} \mid \neg\exists f' \in{\cal F},
f' \neq f, f \models f' \}$,
and $\mathit{Files}$ does not change (see Figure 3).

Figure 3: querying and navigating in a taxonomy
} \end{figure}

A security model

One of our goals in designing LISFS was to be as much compatible as possible with existing file systems. So, there is no reason to change the way file permissions are handled beyond what is implied by the new navigation paradigm. This is quite easy to do for files, because their nature does not change with LISFS, but we had to design a new security model for directories, because their nature changes a lot with LISFS.

Trying to mimic the way hierarchical file systems handle directory permissions introduces the following question: what are the permission/ownership of a conjunction of property names such as man/ps? Under hierarchical file systems, it is the permission/ownership of the last directory on the path (ps in the example) that gives the permission/ownership of the whole path. This is sensible since creating a file in ps does not modify the content of man. However, in the logic file system, the order of property names in a path is immaterial. So, the permissions of man/ps should be a conjunction of the permissions of man and ps to make it certain that, if the owner of man has so decided, nobody can create a new object in man or man/ps. This way of handling directory permissions makes commands that create objects (e.g., touch or mkdir) safe.

LISFS permits that a user ``sees'' a file, without specifying all its keywords; it suffices to specify enough keywords so that the file is designated unambiguously. This is like seeing any distant subdirectory in a hierarchical file system. This has strange side effects on security; one can see a file one does not own, from a directory where one has the write permission. This means that, under the traditional security semantics, one could either delete this file, even without the write permission on all the properties describing this file, or add a property to the description of a file (with the mv command). So, we refine the security model by allowing only the owner of a file to delete or change the property list of a file. The same kind of difficulty arises in hierarchical file systems with the directory /tmp. Every user can create files in /tmp, which implies the write permission for all on /tmp. However, one does not want that a user deletes the files of another user. The ``sticky-bit'' solves this difficulty. In a directory having this bit, the system does not look only at the permissions of the directory, but also at the uid of the requesting process in order to check whether the owner of the file is the same user as the owner of the current process.

To summarize,
$\bullet  $ touch and mkdir security semantics rely on the fact that the permissions of a path are the conjunction of the permissions of each path elements.
$\bullet  $ rm, rmdir, chmod, and chown operations check that the owner of the current process is the owner of the file or property name;
$\bullet  $ mv checks that the owner of the current process is the owner of the file, and check that one has the write permission on the properties one deletes, and the write permission on the properties one adds.

Algorithms and data structures

We will first describe the LS algorithm that computes the answer to ls as it is the most original LISFS operation. Indeed, it computes the sets $\mathit{Files}$ and $\mathit{Dirs}$ for getting local files and sub-directories (see definition of $\mathit{Dirs}$ and $\mathit{Files}$ in Section 2.2), whereas the usual operation performed by ls is merely to read a directory file. Moreover, this operation dictates the choice for the data structures that we will present just after. Then we will give an overview of the concrete implementation of a few representatives LISFS operations.

The LS algorithm

Each property name in ${\cal F}$ is represented by an internal keyword identifier $f_i$, and each object in ${\cal O}$ is represented by an internal object identifier $o_i$. To represent the description $d(o)$ of an object, a table object->keywords indexed by internal object identifiers contains lists of internal keyword identifiers (indeed, in this prototype, the description of an object is a conjunction of properties). This corresponds to rows of the $\mathit{name}\times\mathit{property}$ matrix. A formula is represented by a list of a union value, which is either an internal keyword identifier, or the tag Or associated with a list of internal keyword identifiers (the operands of the disjunction), or the tag Not associated with an internal keyword identifier. The list will play the role of the conjunction. The axioms of the taxonomy are represented as a DAG of internal keyword identifiers implemented as a table keyword->children and a table keyword->parents. We call it the taxonomy DAG. Adding an axiom $x \models a\wedge b
\wedge c$ means attaching $x$ as a child of the internal keyword identifiers $a$, $b$ and $c$. In this data structure, the top node represents the most general property, i.e., $\mathit{true}$ because anything implies $\mathit{true}$.

Extensions are not computed with table object->keywords; instead, they are pre-computed in the inverted table keyword->objects. This uses standard indexing techniques, and this corresponds to the columns of the $\mathit{name}\times\mathit{property}$ matrix. The extension of $a \wedge b$ is computed by intersecting $a$ and $b$ extensions, i.e.,  $\mathit{ext}(a \wedge b) = \mathit{ext}(a)\cap\mathit{ext}(b)$. Similarly, $\mathit{ext}(a \vee b) = \mathit{ext}(a)\cup\mathit{ext}(b)$, and $\mathit{ext}(a \wedge \neg b) = \mathit{ext}(a)\backslash\mathit{ext}(b)$. The underlying assumption for using an inverted table is that the number of attributes per path and file description is small wrt. the number of files. This is confirmed by experiments, and by analysis. Indeed, most of the attributes are computed automatically by functions that do not depend on the number of files (see transducers in Section 4).

In fact, the table keyword->objects does not contain for every property only the objects described by this property, but also the objects that are described by some sub-property of this property. This amounts to precompute the extensions of all the atomic properties. Then, adding a file with property $x$ requires to update (using table keyword->parents) the keyword->objects entry of $x$ and of all its ancestors in the taxonomy DAG. This is costly, but we think that the main operation to optimize is consultation rather than creation. So, we prefer to transfer the hard-work to creation commands. Moreover, creation does not require an immediate update; its response time can be good anyway if it leaves the computation to a background task.

So, table keyword->objects returns extensions at a constant cost. The PWD formula is usually small, because it is often the trace of a navigation by a human being. So, to compute extension(PWD) costs only a few accesses ($\Vert PWD\Vert$) to table keyword->objects. Moreover, extensions can be compressed by using an interval representation (where lists of consecutive elements are represented by their $\mathit{min}$ and $\mathit{max}$), and specialized set operations can be used. This is especially useful for the most general properties which contain in their extensions nearly all the objects.

The algorithm first locates the internal keyword identifiers that are present in PWD, and computes the extension of PWD. Then it searches the taxonomy tree downwards from the root node while the extensions of traversed nodes contain the PWD extension. The highest internal keyword identifiers whose extension does not contain the PWD extension, and has a non empty intersection with it, are the maximal increments to list in $\mathit{Dirs}$ (see pseudo-code below). This avoids having to go through all the formulas. The extensions of all sub-properties of a property that does not intersect with the PWD are not considered, because if keyword->objects[$x$] does not intersect with PWD then no descendant of $x$ will intersect since keyword->objects[$x$] contains the ``in-lined'' extensions of its children. Similarly, extensions of sub-properties of a property that strictly intersects with the PWD are not considered either, because those sub-properties will also intersect with the PWD but are not maximal increments.

Figure 4: the final algorithm
} \end{figure}

extension(f) = 
  let set = 
    forall x in the list f
      compute intersection of 
        either x is a single keyword 
          then keyword->objects[x]
        either x is a OR with elements xs 
            forall y in xs
              compute the union 
                of keyword->objects[y]
        either x is a Not y 
          then do nothing
  in ext := set
     forall x in the list f
       if x is a Not y then 
         ext := minus(ext, 
       otherwise do nothing
    return ext

LS(f) = 
  let ois  = extension(f)
  let fis  = 
    collect keywords fi 
      from the taxonomy DAG
      using a depth first search 
      starting from the top node
      using keyword->children 
        card = cardinality(
        0 < card < cardinality(ois)
  in Dirs = fis
     Files = minus(ois, 
Figure 4 illustrates this algorithm. The context is the same as in Figure 3. Every node of the taxonomy is represented by its name and extension (e.g.,  ${{/}{:}[1{.}{.}7]}$ for the root node). The thick lines represent the taxonomy. The two thin arrows point to the atomic formulas of the current PWD. The dashed arrows represent the actual traversal done by the algorithm for answering command ls. For each traversed node that has subnodes, the algorithm compares the extension of the node with the extension of the PWD; the result of this comparison is written under the node description (e.g.,  $\supseteq ext($PWD$)$ for the root node). Only when the result is $\supseteq ext($PWD$)$ does the algorithm enter the subnodes. Sub-directories are those that do not contain PWD but have a non-empty intersection with it (e.g., ${a_0}$). ${a_6}$ does not count as a sub-directory because it does not intersect PWD. Files in the extension of PWD but not in a sub-directories are returned (i.e., ${\{3,5\}}$).

The complexity of computing $\mathit{Files}$ and $\mathit{Dirs}$ is essentially the complexity of the product of a $\mathit{name}\times\mathit{property}$ matrix by a vector of properties (the PWD), plus the product of the transposed matrix by a vector of names (the $\mathit{Files}$). The first product computes the $\mathit{Files}$ as of the boolean file system (see Section 2.1), and the second product computes the $\mathit{Dirs}$. Assume there are $P$ properties and $N$ names. Both products cost $P\times N$ operations. However, both the matrix and the vector of properties are sparse. Indeed, the number of attributes of a file seldom depends on the number of files. So, one can assume it is constant. This is confirmed by experiments; e.g., the coding of a set of man pages as a logic file system costs about 45 attributes per file whatever the number of files. Let us call $p$ the number of properties per file, $p\ll P$. Moreover, the vector of properties (the PWD) has usually less non-zero elements than $p$. Let us call it $\pi$, $\pi<p$. Then the first product costs only $\pi\times N$. It results in a vector of names with $n$ non-zero elements, $n\ll N$. The second product costs $P\times n$. Finally, since the scalar operations are boolean operations, they have good absorbing properties (e.g.,  $x\wedge
\mathit{false}=\mathit{false}$ whatever is $x$). This allows to short-circuit the computation, without computing every internal products. The essence of algorithm LS is to perform these matrix-vector products, taking advantage of sparseness and taxonomy axioms, and short-circuiting products as soon as possible. In summary, assuming the number of properties per name does not depend on the number of names, the worst-case complexity of algorithm LS is in ${\cal O}(N)$. It is much less in practice because of the caching of answers and the short-circuits in boolean operations. Note that the total number of files, $N$, must be considered with respect to the current PWD. Assume a machine with a million files; this number only affects operations at the root of the file system. As soon as one moves to subdirectories with smaller extensions, $N$ decreases. Section 5 presents the measured cost of LISFS operations on a prototype.

Data structures

Figure 5: the main tables
} \end{figure}

The internal data structures use internal object and keyword identifiers, instead of plain names. This is more space and CPU efficient. In a traditional file system, these internal identifiers are the inode number of a file (or of a directory). In our specification, $o$'s are such internal object identifiers. The meta-data consists mainly in tables keyword->children (which represents the taxonomy DAG) and keyword->objects (which represents the extension table) used by the LS algorithm. Moreover, in order to preserve the consistency of extensions, command touch updates the entries in keyword->objects for all the ancestors of the internal keyword identifiers in the current PWD. This is done recursively using a table keyword->parents. Similarly, command rm file updates the extensions of all internal keyword identifiers used in the description of file. This uses a table object->keywords which was called $d(o)$ in our specification. All these tables are presented in Figure 5.

In order to return plain names in ls answers, we introduce two tables to get the name of a file or property given its internal identifier: keyword->keywordname and object->filename. As we allow to do cd $x$ even if $x$ is not a subdirectory of the current directory, we need to know to what internal identifier $x$ corresponds to, so we add a table keywordname->keyword. In order to check, when adding a file, that there is no other file with the same name and same description, we also introduce a table filename->objects.

In the UNIX context, a typical user manipulates files with names, whereas the operating system uses an internal identifier: the inode number. The inode structure is presented in Figure 6. Traditional file systems store on disk an inode_table (indexed by the inode number) where each entry (aka. the inode) contains the control information to manage a file or a directory, such as its mode, permissions, data block addresses, etc.

Figure 6: the inode
} \end{figure}

Algorithm LS starts from the PWD formula, which it knows only as an inode. So, we add a pwd field to directory inodes, which contains a list of a union type. Each union value contains either the internal property identifier of a property, or the tag Or with a list of internal property identifiers (the operands of the disjunction), or the tag Not with an internal property identifier.

Moreover, answers to ls are cached. Directory inodes contain the addresses of blocks that store the result computed by ls, which mimics the contents of a directory under a hierarchical file system. As this result must be recomputed every time someone modifies the contents of a LISFS, inodes contain a local timestamp indicating the time the current result was computed. LISFS maintains a global timestamp that is incremented every time someone adds or deletes a file or an atomic property. Operation ls compares the global timestamp with the local one, to decide if the increments must be recomputed.

All this means that every time new increments are computed, fresh inodes are allocated, and their local timestamp is set to 0 (to force the next call to ls to recompute increments). When increments are recomputed, LISFS will fill in storage blocks with the result computed by the LS algorithm, and will adjust accordingly the block addresses in the inode.

Concrete operations

We switch now from shell operations to file system operations to enter in more details. We use Linux VFS terminology (Virtual File System [10]). To save space, we enter in the details of only a few LISFS operations. Note that these details are often direct consequences of the data structures, so that details of other operations can be inferred for a large part.

For fault-tolerance, we use journaling for implementing transactions. Every time several tables must be updated in an atomic way, a transaction is started, which logs the updates. In case of failure, the next reboot will redo (or undo) the log (see [16] on journaling and transaction). For example, renaming a keyword needs to update table keyword->keywordname and its inverse table keywordname->keyword. This must be done in a transaction to ensure that the two tables are coherent.

File system operations can be divided into three groups: global operations (aka. superblock operations), directory operations (aka. inode operations), and file operations. Global operations deal with the management of the file system: (un)mounting, and statistics. Directory operations deal with navigation/querying, and creation/deletion of files/properties. Finally, the file operations deal with the file contents. Note that in hierarchical file system, operation readdir is considered as a file operation, though we consider it as an inode operation. This is because in these systems to readdir is actually to read the content of a directory file, though in our case it implies a real computation. Directory operations are original wrt. hierarchical file systems, whereas file and global operations are mostly similar to their counterparts in hierarchical file systems.

Global operations

Operation read_super is called via the user program mount. It locates the different tables on the disk, it stores this information in the superblock structure for further use, and it fills in the first entry in inode_table that corresponds to the root inode. The pwd field of this entry is set to a list of one element containing a single internal identifier of the top node of the taxonomy DAG which corresponds to property $true$. The local timestamp is set to 0, and the global timestamp is set to 1.

Operations put_super (shell command umount), write_super and stat_fs (shell command df) are like their counterparts in hierarchical file systems.

Inode operations

Operation readdir is called via the user program ls. It takes as a parameter an inode, and returns a list of pairs containing the plain name of a file or property and the corresponding inode number. If the global timestamp is equal to the local timestamp of the inode, then the list of pairs is read at the block addresses in the inode. Otherwise, an actual computation must be started: algorithm LS (described in Section 3.1) is applied to the pwd field of the parameter inode. The result is a pair Dirs and Files which contain respectively a list of internal keyword identifiers and a list of internal object identifiers. Then, a new buffer is allocated, and for each oi in Files the pair (object->filename[oi], oi) is stored in the buffer. For each fi in Dirs, a fresh inode number ino is allocated, and the pair (keyword->keywordname,ino) is also stored in the buffer. Then, for each ino, the pwd field of inode_table[ino] is filled in with the conjunction of fi and the current PWD, and its local timestamp is set to 0. Finally, previous blocks used by the contents of this directory are freed, new blocks are allocated and filled with the contents of the current buffer, the local timestamp is set to the value of the global timestamp, and the list of pairs contained in the buffer is returned.

Operation lookup can be called via the command cd keyword. It takes as a parameter an inode (the current directory) and a string s, which is the plain name of a file or of a formula, and it returns the corresponding inode, or an error condition. Operation readdir is called to look if the string is in the list of pairs, in which case the corresponding inode is returned. Otherwise, the string must correspond either to a property not listed as an increment of the current directory, or to a complex formula. If the string has the form of a single name, then keywordname->keyword[s] gives the corresponding internal keyword identifier fi. If this entry is empty, an error code is returned, otherwise a fresh inode ino is allocated, and the pwd field of inode_table[ino] is filled in with the conjunction of fi and of the PWD of the current directory, and inode ino is returned. If the string has the form of a disjunction x|y|z|..., the atomic property names are translated into their internal keyword identifier xi, yi, zi, ...If there is a property name without a corresponding internal keyword identifier, then an error code is returned. Otherwise, a fresh inode is allocated as above, putting this time in the pwd field a conjunction of the PWD of the current directory with the disjunction (with the Or tag) of the internal keyword identifier xi, yi, zi, .... If the string has the form of a negation !x, then the operation is similar, but putting this time in the pwd field the tag Not.

Operation create can be called via command touch. It takes as a parameter an inode (the current directory), a string s, and returns the inode corresponding to the object just created. The current directory must correspond to a conjunction, fis, of atomic properties (as we have restricted the description of object). This is checked first. Then a similar check is done for the string s; it must not contain connectives |, & or !. Finally, another object y with the same name and description must not exist. This is checked by looking for an object y in table filename->objects[s], and comparing fis with the corresponding entry in object->keywords[y]. If one of those checks fails, then an error code is returned, otherwise a transaction is started for updating several tables in an atomic way. Then, a fresh internal object identifier o is allocated. It is added in filename->objects[s], object->filename[o] is set to s, object->keywords[o] is set to fis, and for each keyword f in fis, o is added to keyword->objects[f] and recursively in all the ancestor nodes of f using table keyword->parents. Then, the transaction is ended, and o is returned.

Operation mkdir behaves the same way as create, but this time for atomic properties. It allocates a fresh keyword internal identifier, fi. Then, it adds new entries in tables keywordname->keyword and keyword->keywordname, and it sets to empty keyword->children[fi] and keyword->objects[fi], and for each atomic property, pi, of the PWD, it adds fi in keyword->children[pi] and pi in keyword->parents[fi].

Operation unlink undoes what has been done in create, checking that the current user is the owner of the file to be deleted.

Operation rmdir behaves the same way as unlink, but for atomic properties.

Finally, operations notify_change and read_inode manage the inode_table, associating the appropriate permissions to an inode, using the security semantic described in section 2.4.

File operations

Operations lseek, read, write, open, release, truncate are standard. For example, operation read takes as a parameter an inode, and a buffer to be filled in. It first gets the inode number oi, looks in inode_table[oi] to get the block addresses of the contents of the file and fills in appropriately the buffer passed in parameter.


Section 2 exposed the core of LISFS. In reality this is only a framework, in which more features can be introduced. We present in this section a part of the features that have actually been introduced in the prototype LISFS. A more complete account, particularly on ACL-like security and variants of navigation, is given in a research report [14].

LISFS includes the possibility of valued attributes. For instance, one can create a directory author:minsky, or size:45. Since related properties can be grouped in a taxonomy DAG, one can gather all properties of the form size:x as sub-concepts of the property size. This increases the readability of ls, which will propose first the coarse categories (size, author, ...) and then the finer sub-categories, that is the valued attributes (size:1, size:2, ...). LISFS supports operations on integer attributes, e.g., cd size:>45 and string attributes, e.g., cd auteur:= m.?in.+y.*. We simulate such a query by constructing a disjunction of all the existing properties that satisfy the condition, e.g., cd (size:46|size:72|...). This is done in the lookup operation.

As many properties can be automatically inferred from the file contents, we designed transducers which are functions that extract attributes and values from file contents (as in the Semantic File System [7]). If attributes are new, they are created on-the-fly. In our prototype, transducers are defined for all the system attributes of a file, e.g., its size and last modification time, but also for its extension, e.g., ext:c is extracted from foo.c. For the sake of experimentation, we have also defined a transducer for MP3 music files, which extracts the genre, author and year from the meta-data encoded in the file. This permits requests like cd genre:Disco/year:1980. We could also easily define other transducers to support more file types, but we prefer to offer the user a simple interface to define his own transducers.

Conceptually, the description of a file is split in two parts: the extrinsic part, made of properties assigned by the user, and the intrinsic part, made of properties inferred by transducers. As the content changes, the intrinsic part changes too. This is done in operations release and notify_change. Intrinsic attributes are not updated by the read or write operations. Indeed, calling the transducers is costly, and we prefer to update the intrinsic attributes only when the user closes a file, that is when doing release. To update the intrinsic part, each transducer is called in turn, with the contents, name and system attributes of the file as arguments.

Experimental results

The current prototype of LISFS is implemented as a user level file system, using PerlFS. We use Berkeley DB [13] to manage the different meta-data (implemented as Btrees). The transactional module of Berkeley DB provides the necessary tools for preventing the possible corruption of meta-data by a crash. Finally, we use the underlying file system (EXT2) to manage the contents of files and tables.

Since there is no similar file system to compare with, a part of our experimentations aims at evaluating the overhead of LISFS with respect to a similar technology file system that implements the standard semantics. Since the LISFS prototype is built upon EXT2 and PerlFS, we evaluate the overhead with respect to these file systems. Another part of our experimentations aims at evaluating the performance of LISFS for tasks it has been designed for, like information retrieval. In this case, it is compared with a user-level application that performs the same task, like command find. We ran several experiments to determine the overhead of using LISFS, both in speed and disk space. The platform for all experiments was a Linux box running kernel 2.4, with a 2Ghz Pentium 4, 750Mb RAM, and a 40 Gb IDE disk.

Table 1: Results of Disk Space Benchmark
  Andrew MP3 Man
NbFiles 860 633 11502
FSize 10 Mb 1772 Mb 246 Mb
TSize 2 Mb 3.1 Mb 43.3 Mb
AvNbAttr 26/23 36/20 21/24
NbAttr 1686 3730 43442
AvFSize 11.6 Kb 2799 Kb 21.4 Kb
SpOverH% 20 % 0.17 % 17.6 %
SpOverH/F 2.3 Kb 4.9 Kb 3.7 Kb
SpOverH/A 1.2 Kb 0.84 Kb 1 Kb
SpOverH/FA 47 bytes 87 bytes 84 bytes

NbFiles = Number of files, FSize = Total size of files, TSize = Total size of LISFS tables, AvNbAttr = Average number of file attributes (intrinsic/extrinsic), NbAttr = Total number of attributes, AvFSize = Average file size, SpOverH% = Space overhead (per cent), SpOverH/F = Average space overhead per file, SpOverH/A = Average space overhead per attribute, SpOverH/FA = Average space overhead per attribute of file.

The first experiment evaluates the disk space overhead used by the meta-data of LISFS (see Table 1). The experiment is run for a set of 633 MP3 music files, a set of 860 program files obtained by ten copies of the Andrew file system benchmark [9], and a set of 11502 man pages. The Andrew files are described by intrinsic attributes valued by the names of functions declared in them (as produced by command CTAGS). The MP3 files are described by intrinsic attributes valued by MP3-specific meta-data such as genre and artist. The man pages are described by the words of their description line. All have extrinsic attributes for ACL-like security and for representing their position in a user-defined hierarchy, plus other intrinsic system attributes for size, last modification time, etc. SpOverH/F measures the average cost of file descriptions (i.e., rows of the $name\times property$ matrix). SpOverH/A measures the average cost of each attribute (i.e., extensions, or columns of the matrix). SpOverH/FA measures the average cost of each individual attribute of each file (i.e. each non-empty position in the matrix).

In the second experiment, we ran the modified Andrew benchmark, first on the native file system (EXT2), then on a hierarchical file system implemented via PerlFS, then on LISFS where transducers were turned off then on (see Table 2). The Andrew benchmark has 5 phases: Mkdir constructs a directory hierarchy, Copy copies files, Scan recursively traverses the whole hierarchy, Read reads every byte of every file, and finally Make compiles the files. Note row Read where LISFS without transducer is faster than PerlFS because PerlFS goes into empty directories that LISFS avoids because they are not relevant. We also ran our own benchmarks that consists in creating the 633 music files, and creating the 11502 man pages. In both cases, creating also means indexing through the use of adhoc transducers.

Table 2: Results of CPU Benchmark
  Ext2 PerlFS LISFS (transducer off) LISFS (transducer on)
Mkdir 0.217s 0.986s 1.823s 3.703s
Copy 1.359s 5.943s 13.212s 46.296s
Scan 2.506s 5.141s 5.348s 6.638s
Read 3.548s 11.510s 11.119s 12.333s
Make 16.896s 28.384s 36.182s 46.260s
Total 24.526s 51.964s 67.684s 115.230s
MP3 2min28s 4min30s 5min 5min30s
Man 22min 29min 44min 85min

We now compare the speed of search-like activities using UNIX find and apropos, and LISFS lookup. With the Andrew files find'ing function GXfind takes 2.899 seconds, but look'ing it up takes 0.081 seconds. Similarly, with the MP3 files find'ing disco music takes 3.292 seconds, but look'ing it up is immediate. Doing ls change with the man pages takes 1.370 seconds, and returns 110 increments. Doing apropos change takes 0.145 seconds, and returns 288 items. In other words, the increments reveal the organization of the items. Doing ls change/directory with the man pages takes 0.026 seconds, and returns 4 entries. Doing apropos change | grep directory takes 0.030 seconds, and returns the same 4 entries. We have also tested the speed of ls in directories of various sizes in the music files context. In directory artist, ls computes 155 increments in 0.405 seconds. In directory size, it computes 629 increments in 1.058 seconds, and finally it computes 28 increments in directory genre in 0.220 seconds. Note that in any case, only the first ls in a given directory takes time, as for further ls LISFS uses its cache, and answers immediately.

Figure 7: creation times (sec)
\begin{figure}\par\centerline{ \psfig{,angle=-90,width=3.125in} }\...
...sfig{,angle=-90,width=3.125in} }\par {\tiny .}

Figure 7 plots the creation times of the 633 MP3 files and 11502 man pages. It shows an almost constant creation time until 10000 files, and then a deterioration. We believe that a better representation of extensions will push this point to a greater value.

In summary, the space overhead is tolerable, and it could still be decreased by using better marshalling techniques. Operations lookup and readdir do not show a great performance penalty, especially considering the work they perform. Moreover, they compare positively with search tools like find. On the opposite, operation create suffers from a large performance penalty, because the extensions of the taxonomy DAG must be checked and updated. This is visible in the Copy row of Table 2. It can be cured by performing most of the create operation lazily, in the background during idle time. Indeed, the result returned by create does not depend on the updating of internal tables. We need also study alternative representations of the taxonomy DAG.

Related work

The Semantic File System (SFS [7]) was the first file system to combine querying and navigation. It remained compatible with the file system interface by using virtual directories. Some attributes were extracted from the contents of the files by transducers. This allowed users to express queries such as cd ext:c/size:15. However, users could not assign their own attributes to a file (i.e., all these attributes were intrinsic, see Section 4). More importantly, querying and navigation were two separated operation modes; one could not navigate in a virtual directory that resulted from a query. The Hierarchy and Contents proposal [8] solved this problem in a way that leads to inconsistencies. Directories that represent answers to queries are no longer virtual; they are real directories in which one can navigate, and even write. However, one can write something which is inconsistent with the query that created the directory. Another file system that combines querying and navigation is the Be file system (BeFS [6]). Following the observation that hierarchical file systems fail to describe a file by a conjunction of independent concepts, BeFS allows the user to manually assign attributes to a file. But this extension is not compatible with a standard UNIX interface (as opposed to SFS and LISFS where one can use classical shells). So, one can either use a shell or browser and navigate, or use the new interface and do querying, but not both. Finally, the Nebula file system [1] allows a user to assign multiple attributes to a file and formulate query at the shell level. One can also create kinds of directories called views, which are just names assigned to queries, as for databases. Views can be organized in a hierarchy, where subviews refine parent's views with another query (restricting the set of objects). This allows to cache frequently used queries. One can both navigate following the subviews links, and query the file system, but as for SFS, one cannot navigate in the result of a query.

The principal contribution of our work is a seamless combination of querying and navigation, under the file system interface. The key features are to combine intrinsic and extrinsic descriptions, and to permit navigation in query results by computing relevant subdirectories.

This idea of combining querying and navigation via increments is not new. In the literature, increments are also called co-occurrence lists, term suggestions, relevant informations, significant keywords, ... In [11], a corpus of keywords is extracted from man pages, and via formal concept analysis [5], a lattice of keywords is computed to permit a user to find man pages by keywords, and getting as a result other keywords considered relevant (as increments in Section 2.2). Queries are limited to conjunctions of keywords, and keywords cannot be ordered which mean we get the disadvantages mentioned in Section 2.3. In [15], a similar approach is applied to bibliographic information retrieval. The querying mechanism of these applications is completely encompassed by LISFS; the answers of LISFS are the answers of formal concept analysis. In fact, the domain of information retrieval is aware of the need for integrating querying and navigation (e.g., see [2,12]). However, the proposals in this domain remain at the application level, and are very often combined with visual interface issues.

All those systems are limited to conjunctive queries, and are more like front-ends over another information system for allowing to combine querying and navigating, which means that they do not handle updating in their interface. Our contribution is to offer all these services, querying, navigating, and updating, at the system level, so that many kinds of application can be built on it.

Future directions

There is much room for performance improvement in the prototype LISFS. E.g., operations create and readdir are too expensive. Tricks such as grouping of commands (as in X Window), amortization, lazy structures or use of idle time will certainly improve the performance of create. Finally, in place of a global timestamp, locating more precisely what is dirty could lead to less cache miss. Another future work is to make a ``complete'' logic file system, allowing arbitrary formulas in object descriptions as well as in queries. This requires to incorporate an automatic theorem prover for representing the $\models$ relation in LISFS. The goal here is to permit an open-ended range of file description styles. Even if some logics are undecidable (e.g. predicate logic) or unpracticable (e.g., propositional logic), there are many useful and practicable logics that could be used as a file description language: e.g., a logic of types for program components, or a logic of intervals for expressing dates. Another direction is to overcome the difference between directories and files. We would like to navigate in files in the same way as in directories. E.g., one would like to navigate inside a BibTeX file, or inside a program source file. Then, a user could do cd !comment & security to get all the parts of a source file that are not comments and that talk about security.


We have presented a new file system paradigm which allows to freely combine high-level file description and querying using logic formulas, navigation, and updating. This is called a Logic File System. The integration of querying and navigation goes beyond previous proposals; coherence is kept when writing in virtual directories, and navigation and querying can be freely intermingled. Such a file system gives at a system level services that are useful in many applications. A key technical aspect is to develop data structures and algorithms that permit to implement a prototype LISFS with encouraging performances. Experiments show that though the prototype LISFS is slower than a more classical one, it passes usual benchmarks with reasonable performances: create-intensive benchmarks show a bad performance ratio for LISFS, but ls-intensive benchmarks show almost no penalty. Consider also that what operation create actually does is on-line indexing. Note also that the current implementation of LISFS is very soft, a user-level program based on PerlFS, and it could be improved by using more effective techniques. We believe that all this confirms the validity of LISFS.


We thank the anonymous referees and our sheperd, Prof. Darrell Long, for their thoughtful remarks. We also wish to acknowledge the collaboration with Sébastien Ferré for elaborating the theory of Logic Information System.


A prototype LISFS and more information on this project can be down-loaded at the following URL:


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A Logic File System

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